Sample classification method, device and storage medium

Abstract

A sample classification method (100), device (500) and storage medium. The sample classification method (100) includes: obtaining a sample image (S1) of a sample to be classified, displaying the sample image on a first display interface (S1, S110); receiving a classification instruction (S120) for classifying the sample to be classified corresponding to the sample image (S1); displaying alternative classification results of the sample to be classified in a peripheral area of the sample image (S1) based on the classification instruction, so that a user can select a final classification result of the sample to be classified (S130); receiving a user selection instruction for the alternative classification result, and obtaining the final classification result of the sample to be classified based on the selection instruction (S140). Displaying the alternative classification results of the sample to be classified in a peripheral area of the sample image (S1) of the sample to be classified allows the user to quickly select the final classification result of the sample to be classified from the alternative classification results, thereby improving the efficiency of sample classification, simplifying user operations, and reducing the possibility of classification errors.

Description

[0001] manual Technical Field

[0002] The present application relates to the technical field of sample classification for film readers, and more specifically to a sample classification method, device, and storage medium. Background Art

[0003] The current blood cell classification method usually involves a film reader automatically scanning, segmenting, and generating blood cell images for a patient's blood sample prepared as a blood smear, and providing the original classification results. On this basis, manual review and reclassification of the blood cell images are performed.

[0004] Currently, users primarily reclassify blood cells using the following methods: 1) dragging a cell image to the corresponding parameter row in the parameter table; 2) dragging a cell image to the image area of the target category; 3) right-clicking the cell image to bring up a drop-down box, from which they select the target category. The first two methods involve long dragging distances and are inconvenient to operate. Furthermore, the first method, with its numerous rows of table parameters, can easily lead to the wrong category. The third method displays all category items in the drop-down box, making it difficult to quickly find and select the target type. This is inefficient and prone to classification errors. Summary of the Invention

[0005] The present application is proposed to solve at least one of the above problems. According to one aspect of the present application, a sample classification method is provided, the method comprising: obtaining a sample image of a sample to be classified, and displaying the sample image on a first display interface; receiving a classification instruction for classifying the sample to be classified corresponding to the sample image; displaying alternative classification results of the sample to be classified in a peripheral area of the sample image based on the classification instruction, so that a user can select a final classification result of the sample to be classified; receiving a user's selection instruction for the alternative classification result, and obtaining the final classification result of the sample to be classified based on the selection instruction.

[0006] According to another aspect of the present application, a sample classification device is provided, which includes a memory, a processor and a display, wherein the memory is used to store programs and sample images of samples to be classified, the display is used to display based on the control of the processor, and the processor is used to run the program in the memory to perform the following steps: obtaining a sample image of the sample to be classified, and displaying the sample image on a first display interface by the display; receiving a classification instruction for classifying the sample to be classified corresponding to the sample image; controlling the display of alternative classification results of the sample to be classified in the peripheral area of the sample image based on the classification instruction, so that the user can select the final classification result of the sample to be classified; receiving a user selection instruction for the alternative classification result, and obtaining the final classification result of the sample to be classified based on the selection instruction.

[0007] According to another aspect of the present application, a storage medium is provided, on which a computer program is stored. When the computer program is run, the computer program executes the above-mentioned sample classification method.

[0008] According to the sample classification method, device and storage medium of the embodiments of the present application, the alternative classification results of the sample to be classified are displayed in the peripheral area of the sample image of the sample to be classified, so that the user can quickly select the final classification result of the sample to be classified from the alternative classification results, thereby improving the efficiency of sample classification, simplifying user operation and reducing the possibility of classification errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flowchart of a sample classification method according to an embodiment of the present application is shown.

[0010] Figure 2 An example of displaying alternative classification results for a sample to be classified according to the sample classification method of an embodiment of the present application is shown.

[0011] Figure 3A and Figure 3B Another example of displaying alternative classification results for a sample to be classified according to the sample classification method of an embodiment of the present application is shown.

[0012] Figure 4A and Figure 4B Another example of displaying alternative classification results for a sample to be classified according to the sample classification method of an embodiment of the present application is shown.

[0013] Figure 5 A schematic block diagram of a sample classification device according to an embodiment of the present application is shown.

[0014] Figure 6 A schematic diagram of the structure of a film reading machine is shown as an application example of the sample classification method according to an embodiment of the present application. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present application more apparent, example embodiments according to the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application described in this application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of this application.

[0016] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present application. However, it will be apparent to those skilled in the art that the present application can be implemented without one or more of these details. In other examples, some technical features well known in the art are not described in order to avoid confusion with the present application.

[0017] It should be understood that the present application can be implemented in different forms and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, providing these embodiments will make the disclosure thorough and complete and will fully convey the scope of the present application to those skilled in the art.

[0018] The purpose of the terms used herein is only to describe specific embodiments and is not intended to limit the present application. When used herein, the singular forms "a", "an", and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0019] In order to thoroughly understand the present application, detailed steps and detailed structures will be provided in the following description to illustrate the technical solution proposed by the present application. The preferred embodiments of the present application are described in detail below. However, in addition to these detailed descriptions, the present application may also have other implementation methods.

[0020] First, refer to Figure 1 To describe the sample classification method according to an embodiment of the present application. Figure 1 FIG. 1 shows a schematic flow chart of a sample classification method 100 according to an embodiment of the present application. Figure 1 As shown, the sample classification method 100 includes the following steps:

[0021] In step S110 , a sample image of a sample to be classified is acquired, and the sample image is displayed on a first display interface.

[0022] In the embodiments of the present application, a sample image of a sample to be classified is displayed on a first display interface. The term "first display interface" is used to distinguish it from the "second display interface" that will be described later and is used to display alternative classification results for the sample to be classified, and has no other limiting meaning. In the embodiments of the present application, the first display interface and the second display interface can be different display interfaces or different states of the same display interface.

[0023] In step S120, a classification instruction for classifying the to-be-classified sample corresponding to the sample image is received.

[0024] In an embodiment of the present application, a classification instruction for classifying a sample to be classified may be received from a user through an input device (such as a touch screen of a display interface or a mouse and keyboard, etc.), such as executing a drag instruction, a long press instruction, a click instruction, etc. on a sample image of the sample to be classified. The drag instruction may include dragging the sample image of the sample to be classified by a preset distance, which may be a relatively small value to avoid the inconvenience and error-prone problems caused by long-distance dragging. Based on the classification instruction, alternative classification results of the sample to be classified may be obtained for display, as will be described in the following steps.

[0025] In step S130 , alternative classification results of the sample to be classified are displayed in a peripheral area of the sample image based on the classification instruction, so that the user can select a final classification result of the sample to be classified.

[0026] In an embodiment of the present application, based on the classification instruction received in step S120, the alternative classification results of the sample to be classified can be first obtained. In one example, the alternative classification results of the sample to be classified can be obtained based on automatic recognition of the sample image of the sample to be classified. In an embodiment of the present application, the alternative classification results of the sample to be classified are displayed in the peripheral area of the sample image of the sample to be classified. Here, the peripheral area of the sample image can be understood as: the area around the sample image within a predetermined distance with the sample image as the center. Figure 2 To describe by way of example.

[0027] Figure 2 FIG. 1 shows an example of displaying alternative classification results for a sample to be classified according to the sample classification method of an embodiment of the present application. Figure 2As shown, the display interface includes sample images of multiple samples to be classified. For one of the sample images S1, after receiving the classification instruction, the alternative classification results are displayed in the surrounding area of the sample image S1, including: basophils, neutrophils, neutrophils, smear cells and sediment. Figure 2 In the example shown, the alternative classification results of the sample to be classified corresponding to the sample image S1 are displayed in a circular area centered on and surrounding the sample image S1. It should be understood that this is merely exemplary. According to the teachings of this application, the alternative classification results of the sample to be classified do not necessarily have to be displayed in a circular area in the peripheral area of the sample image of the sample to be classified. As long as it is in the vicinity of the sample image, the user can select it conveniently and quickly. In general, compared with the method of dragging the sample image a long distance for classification, the method provided by this application of displaying the alternative classification results of the sample to be classified in the peripheral area of the sample image of the sample to be classified can enable the user to quickly select the final classification result of the sample to be classified from the alternative classification results, thereby improving the efficiency of sample classification, simplifying user operations, and reducing the possibility of classification errors.

[0028] In an embodiment of the present application, the alternative classification results of the sample to be classified can be displayed on the second display interface. As mentioned above, the first display interface displaying the sample image of the sample to be classified and the second display interface displaying the alternative classification results of the sample to be classified can be different display interfaces or different states of the same display interface.

[0029] In one embodiment of the present application, the alternative classification results of the sample to be classified displayed in the peripheral area of the sample image may include at least one classification result obtained by classifying the sample to be classified corresponding to the sample image, and each alternative classification result may be sorted and displayed based on its respective classification confidence. The classification confidence of a classification result refers to the probability that the true classification result of the sample to be classified is the classification result. For example, after classifying a sample image of a white blood cell, one of the alternative classification results obtained is a neutrophil, and the classification confidence is 90%, which means that the probability that the white blood cell is a neutrophil is 90%. In this embodiment, each alternative classification result is sorted and displayed according to its respective classification confidence. Since the higher the classification confidence of a classification result, the more likely it is that the classification result is a true classification result, the display method based on the classification confidence can enable the user to find the final classification result of the sample to be classified more quickly, thereby improving classification efficiency.

[0030] In one example, the higher the classification confidence, the closer the corresponding classification result is displayed to the sample image, which can further improve the speed at which the user finds and selects the final classification result of the sample to be classified. In another example, the alternative classification results of the sample to be classified displayed in the peripheral area of the sample image may include at least two levels of classification results, wherein the classification confidence of each of the previous level classification results is higher than the classification confidence of each of the next level classification results. Each level of classification results can be displayed step by step based on user instructions, so as to obtain the final classification result of the sample to be classified based on at least one level of classification results. Figure 3A and Figure 3B To describe this example.

[0031] Figure 3A and Figure 3B Another example of displaying alternative classification results for a sample to be classified according to the sample classification method of an embodiment of the present application is shown, wherein: Figure 3A Shows the first-level classification results of the samples to be classified, Figure 3B The second level classification results of the sample to be classified are shown. In this example, instead of displaying all the alternative classification results of the sample to be classified at once, a hierarchical menu is displayed, where the first level classification results display the first few classification results with the highest classification confidence, such as Figure 3A Eosinophils, basophils, neutrophils, lymphocytes, and monocytes are shown. If the user believes that the first-level classification results do not contain the true classification results of the sample to be classified or is unsure whether the first-level classification structure contains the true classification results of the sample to be classified, the second-level classification results can be triggered to pop up. The second-level classification results display several classification results with the second highest classification confidence, such as Figure 3B As shown, primitive, neutrophilic late immature, atypical lymphoid, and immature mononuclear. It should be understood that this is only exemplary, and in actual application, the displayed alternative classification results may include more than two levels of classification results.

[0032] In the embodiment of the present application, when the user triggers the display of the next level classification results, he can execute a hover command, a long press command, or a click command on any of the previous level classification results. Alternatively, an "other" option can be provided in the previous level classification results so that the user can trigger the next level classification results through the "other" option, such as Figure 3A In the embodiment of the present application, the alternative classification results may also include an "unclear classification" option, such as Figure 3B As shown, this option can be used in scenarios where the user is unsure of the final classification result. The hovering instruction includes a selection method of hovering a pointer over a preset area of a selected object to select the object.

[0033] In an embodiment of the present application, the previous level classification result can be closer to the sample image than the next level classification result. For example, the next level classification result can be displayed in the surrounding area of the previous level classification result. Figure 3B As shown, the second-level classification results are displayed in the surrounding area of the first-level classification results. This display method is clear and hierarchical and allows users to make more flexible choices, such as returning to the first-level classification results from the second-level classification results. Of course, this is only exemplary. It is also possible to display the previous level classification results without displaying the previous level classification results when displaying the next level classification results, and provide an option to return to the previous level classification results.

[0034] In one example, the first level classification result of the aforementioned at least two levels of classification results can be displayed in at least a portion of the area surrounded by the first circumference, and for the remaining level classification results of the at least two levels of classification results, the latter level classification results are displayed around the former level classification results in at least a portion of the area surrounded by the second circumference, the first circumference is concentric with the second circumference and the radius of the second circumference is greater than the radius of the first circumference, for example Figure 3B In this example, the classification results are displayed step by step in a circular display. The circular shape has high area utilization, is convenient for grading, and is visually neat, so that the user can clearly see the final classification result of the sample to be classified, and can quickly and accurately select it.

[0035] In general, the above example of displaying classification results based on classification confidence levels eliminates the need for users to select the final classification result of the sample to be classified from numerous options at once. Instead, users may find the final classification result among a smaller number of alternative classification results, further improving classification efficiency.

[0036] In a further embodiment of the present application, regardless of whether or not a hierarchical display is performed, in an embodiment in which alternative classification results are displayed based on classification confidence, the display can be further performed in the following manner: the classification result with the highest classification confidence is displayed in a manner that is distinguished from classification results with other classification confidences, or classification results with different classification confidences are displayed separately. Exemplarily, the differentiated display includes at least one of the following: displaying classification results with different classification confidences with icons of different colors; displaying classification results with different classification confidences with icons of different sizes; displaying classification results with different classification confidences with icons at different distances from the sample image; displaying classification results with different classification confidences with text of different attributes; displaying classification results with different classification confidences in areas with different shapes; and displaying the numerical value of the classification confidence of each classification result on the corresponding classification result. In this embodiment, the classification result with the highest classification confidence is displayed in a manner that is different from the classification results with other classification confidences, or the classification results with different classification confidences are displayed separately from each other. This can further enhance the user's visual effect, allowing the user to more clearly see the classification results with high classification confidence. This can not only further improve the classification efficiency, but also further reduce the possibility of classification errors due to such differentiated display.

[0037] In another embodiment of the present application, the alternative classification results of the sample to be classified displayed in step S130 may include at least two levels of classification results, wherein at least one level of classification results is divided into multiple groups, and the multiple groups include all possible classification results in the classification level, and the classification items included in any group in the previous level of classification results are the next level of classification results, and the display of the alternative classification results includes: displaying each level of classification results step by step based on user instructions, so as to obtain the final classification result of the sample to be classified based on at least one level of classification results. In this embodiment, instead of displaying all the alternative classification results of the sample to be classified at once, it is displayed based on a hierarchical menu, wherein the first level classification result displays the grouping result of the sample to be classified, and the second level classification result displays the lower level result of any group in the first level classification result, that is, the grouping or classification result further included in the group, and so on. The following is combined with Figure 4A and Figure 4B To describe this example.

[0038] Figure 4A and Figure 4B FIG2 shows another example of displaying alternative classification results for a sample to be classified according to the sample classification method of an embodiment of the present application, wherein: Figure 4A Shows the first-level classification results of the samples to be classified, Figure 4B The second level classification results of the samples to be classified are shown in FIG. In this example, the first level classification results show the categories of the groups to which the samples to be classified may belong, such as Figure 4AThe eosinophil group, basophil group, neutrophil group, monocyte group, and lymphocyte group are shown. The user can select the group to which the sample to be classified belongs as the final classification result based on the first-level classification result, or continue to trigger the second-level classification result and accurately select the next-level category of a group in the first-level classification result, such as Figure 4B It should be understood that this is only exemplary, and in actual applications, the displayed alternative classification results may include more than two levels of classification results.

[0039] Similar to the above embodiment, in this embodiment, when the user triggers the display of the next level classification results, he can execute a hover command, a long press command, or a click command on any of the previous level classification results. Alternatively, an "other" option can be provided in the previous level classification results so that the user can trigger the next level classification results through the "other" option, such as Figure 4A In the embodiment of the present application, the alternative classification results may also include an "unclear classification" option, such as Figure 4B As shown, this option can be used in scenarios where the user cannot be sure of the final classification result.

[0040] In an embodiment of the present application, the previous level classification result may be closer to the sample image than the next level classification result, and the next level classification result may be displayed in the surrounding area of the previous level classification result, for example. Figure 4B As shown, the second-level classification results are displayed in the surrounding area of the first-level classification results. This display method is clear and hierarchical and allows users to make more flexible choices, such as returning to the first-level classification results from the second-level classification results. Of course, this is only exemplary. It is also possible to display the previous level classification results without displaying the previous level classification results when displaying the next level classification results, and provide an option to return to the previous level classification results.

[0041] In one example, the first level classification result of the aforementioned at least two levels of classification results can be displayed in at least a portion of the area surrounded by the first circumference, and for the remaining level classification results of the at least two levels of classification results, the latter level classification results are displayed around the former level classification results in at least a portion of the area surrounded by the second circumference, the first circumference is concentric with the second circumference and the radius of the second circumference is greater than the radius of the first circumference, for example Figure 4B In this example, the classification results are displayed step by step in a circular display. The circular shape has high area utilization, is convenient for grading, and is visually neat, so that the user can clearly see the final classification result of the sample to be classified, and can quickly and accurately select it.

[0042] In a further embodiment of the present application, the aforementioned combination Figure 4A and Figure 4BThe classification items included in each of the at least two levels of classification results in the candidate classification results can also be displayed separately according to the level of classification confidence. In this embodiment, each classification item included in each level of classification results represents a possible grouping result or classification result of the sample to be classified, and the result also has a confidence level. The classification items are displayed separately based on the confidence level, which can further enhance the user's visual experience and improve the efficiency of the user's selection of classification results.

[0043] In general, the aforementioned examples of displaying classification results in a hierarchical manner based on classification confidence and / or grouping can improve the efficiency of user information screening. The next level is expanded only when the previous level does not provide the target classification results, or the previous level provides guidance information to the next level, so that users do not need to screen a large amount of information at the same time, thereby further improving classification efficiency.

[0044] In other embodiments of the present application, the classification instruction received in step S120 may also include an instruction to enter a classification keyword; in this embodiment, the candidate classification results for the sample to be classified displayed in step S130 may include classification results based on the classification keyword. In this embodiment, the corresponding candidate classification results are displayed based on the classification keyword entered by the user, and the number of displayed candidate classification results can also be reduced, allowing the user to quickly select the final classification result for the sample to be classified.

[0045] In a further embodiment of the present application, the alternative classification results of the sample to be classified displayed in step S130 may also include classification results obtained based on historical data, and the historical data include the final classification results of the previous sample. In this embodiment, the user's selection of the final classification result of the previous sample is used as historical data for the classification of the current sample, so that the current sample can be classified in combination with the user's historical operations, which can further increase the possibility that the displayed alternative classification result is the correct classification result (real classification result), thereby further improving the classification efficiency. Exemplarily, relative to the aforementioned classification results displayed based on classification confidence ranking, the display position of the classification results obtained here based on historical data can be closer to the sample image of the sample to be classified; or, relative to the classification results obtained here based on historical data, the display position of the aforementioned classification results displayed based on classification confidence ranking is closer to the sample image of the sample to be classified.

[0046] Now return to reference Figure 1 , describing the subsequent steps of the sample classification method 100 according to an embodiment of the present application.

[0047] In step S140 , a user selection instruction for the candidate classification result is received, and a final classification result of the sample to be classified is obtained based on the selection instruction.

[0048] In an embodiment of the present application, based on the candidate classification results for the sample to be classified displayed in step S130, the user can select the final classification result of the sample to be classified from among them through a selection instruction. For example, the selection instruction may include a long press instruction, a click instruction, etc. on any of the candidate classification results. Based on the user's selection instruction, the final classification result of the sample to be classified can be obtained. In one example, the final classification result of the sample to be classified can be displayed.

[0049] In a further embodiment of the present application, method 100 may also include (not shown): after obtaining the final classification result of the current sample to be classified, performing any one of the following operations on the remaining samples in the same initial group as the current sample to be classified: automatically displaying the alternative classification results of the remaining samples in the same initial group; generating the final classification results of the remaining samples in the same initial group based on the final classification result of the current sample to be classified; wherein, the initial grouping is generated based on the initial classification result of the sample, the current sample to be classified is a sample to be reclassified, the reclassification is a verification or correction of the initial classification result, and the reclassification result obtained after the sample is reclassified is the final classification result.

[0050] In this embodiment, the sample classification method 100 of the present application is used to reclassify samples, that is, the samples have been initially classified before reclassification, and multiple samples may be initially classified into the same group. In this case, for any sample in the same group, after being reclassified according to the sample classification method 100 according to the embodiment of the present application, the presentation of alternative classification results for other samples can be automatically started without inputting classification instructions for the sample images of other samples in the same group. It is also possible that the final classification result of a reclassified sample in the group can be directly used as the final classification result of other samples without even repeating the process of the sample classification method 100 according to the embodiment of the present application for other samples. This can further improve the efficiency of classifying multiple samples.

[0051] Based on the above description, the sample classification method according to the embodiment of the present application displays the alternative classification results of the sample to be classified in the peripheral area of the sample image of the sample to be classified, so that the user can quickly select the final classification result of the sample to be classified from the alternative classification results, thereby improving the efficiency of sample classification, and the user operation is simple, reducing the possibility of classification errors. In addition, the sample classification method according to the embodiment of the present application can also display the alternative classification results of the sample to be classified in a hierarchical manner, which can improve the efficiency of user information screening. Only when the previous level does not provide the target classification result, the next level is expanded, or the previous level provides guidance information to the next level, so that the user does not need to screen a large amount of information at the same time, thereby further improving the classification efficiency. In addition, the sample classification method according to the embodiment of the present application can also distinguish and display the alternative classification results of the sample to be classified with different classification confidences, which can further enhance the user's visual effect, so that the user can more clearly see the classification results with high classification confidence, which can not only further improve the classification efficiency, but also further reduce the possibility of classification errors due to such differentiated display.

[0052] The above example shows the sample classification method according to the embodiment of the present application. Figure 5 A sample classification device according to another aspect of the application is described. Figure 5 FIG. 5 shows a schematic block diagram of a sample classification device 500 according to an embodiment of the present application. Figure 5 As shown, the sample classification device 500 may include a memory 510, a processor 520, and a display 530. The memory 510 stores a program for implementing the corresponding steps in the sample classification method 100 according to an embodiment of the present application. The processor 520 is used to run the program stored in the memory 510 to execute the corresponding steps of the sample classification method 100 according to an embodiment of the present application, and the display 530 is used to display information based on the control of the processor 520. Those skilled in the art can understand the structure and operation of the sample classification device 500 in combination with the above description. For the sake of brevity, only the main operations of the processor 520 are described here, and some details above are not repeated.

[0053] In one embodiment of the present application, when the computer program is executed by the processor 520, the processor 520 performs the following steps: obtaining a sample image of the sample to be classified, and displaying the sample image on a first display interface by the display 530; receiving a classification instruction for classifying the sample to be classified corresponding to the sample image; controlling the display of alternative classification results of the sample to be classified in a peripheral area of the sample image based on the classification instruction, so that the user can select the final classification result of the sample to be classified; receiving a user selection instruction for the alternative classification result, and obtaining the final classification result of the sample to be classified based on the selection instruction.

[0054] In one embodiment of the present application, the candidate classification result is obtained by the processor 520 identifying the sample image.

[0055] In one embodiment of the present application, the candidate classification results include at least one classification result obtained by classifying the sample to be classified corresponding to the sample image, and each of the candidate classification results is sorted and displayed based on its own classification confidence.

[0056] In one embodiment of the present application, the sorting display includes: the higher the classification confidence, the closer the display position of the corresponding classification result is to the sample image.

[0057] In one embodiment of the present application, the alternative classification results include at least two levels of classification results, wherein the classification confidence of each of the previous level classification results is higher than the classification confidence of each of the next level classification results, and the sorting display includes: displaying each level of classification results step by step based on user instructions, so as to obtain the final classification result of the sample to be classified based on at least one level of classification results.

[0058] In one embodiment of the present application, when the alternative classification results or any level of classification results among the alternative classification results are displayed on the display 530, the classification result with the highest classification confidence is displayed in a manner that is different from the classification results with other classification confidences, or the classification results with different classification confidences are displayed separately.

[0059] In one embodiment of the present application, the differentiated display includes at least one of the following: displaying classification results with different classification confidences with icons of different colors; displaying classification results with different classification confidences with icons of different sizes; displaying classification results with different classification confidences with icons at different distances relative to the sample image; displaying classification results with different classification confidences with text of different attributes; displaying classification results with different classification confidences in areas with different shapes; and displaying the numerical value of the classification confidence of each classification result on the corresponding classification result.

[0060] In one embodiment of the present application, the alternative classification results include at least two levels of classification results, wherein at least one level of classification results is divided into multiple groups, and the multiple groups contain all possible classification results in this level of classification, and the classification items included in any group in the previous level of classification results are the next level of classification results. The display 530 displays the alternative classification results including: displaying each level of classification results step by step based on user instructions, so as to obtain the final classification result of the sample to be classified based on at least one level of classification results.

[0061] In one embodiment of the present application, the classification items included in each level of classification results in the at least two levels of classification results are displayed according to the level of classification confidence.

[0062] In one embodiment of the present application, the latter level classification result is displayed in a peripheral area of the former level classification result.

[0063] In one embodiment of the present application, the first-level classification result of the at least two-level classification results is displayed in at least a partial area surrounded by a first circle, and for the remaining levels of classification results of the at least two-level classification results, the latter-level classification results are displayed around the former-level classification results in at least a partial area surrounded by a second circle, and the first circle is concentric with the second circle and the radius of the second circle is greater than the radius of the first circle.

[0064] In one embodiment of the present application, the previous level classification result is closer to the sample image than the next level classification result.

[0065] In one embodiment of the present application, the candidate classification results are displayed on a second display interface, and the first display interface and the second display interface are different display interfaces.

[0066] In one embodiment of the present application, the candidate classification results and the sample images are displayed in different states of the first display interface.

[0067] In one embodiment of the present application, the classification instruction includes any one of the following instructions executed on the sample image: a drag instruction, a long press instruction, and a click instruction.

[0068] In one embodiment of the present application, the classification instruction includes an instruction to input a classification keyword, and the candidate classification results include classification results based on the classification keyword.

[0069] In one embodiment of the present application, the user instruction includes the classification instruction and any one of the following instructions executed on any one of the previous level classification results: a hover instruction, a long press instruction, and a click instruction.

[0070] In one embodiment of the present application, the display 530 is further configured to display the final classification result of the sample to be classified.

[0071] In one embodiment of the present application, the candidate classification results displayed for the sample to be classified also include classification results obtained based on historical data, and the historical data includes final classification results of previous samples.

[0072] In one embodiment of the present application, relative to the classification results displayed based on classification confidence ranking, the display position of the classification results obtained based on historical data is closer to the sample image; or relative to the classification results obtained based on historical data, the display position of the classification results displayed based on classification confidence ranking is closer to the sample image.

[0073] In one embodiment of the present application, when the computer program is executed by the processor 520, it also causes the processor 520 to perform the following steps: after obtaining the final classification result of the sample to be classified, perform any one of the following operations on the remaining samples in the same initial group as the sample to be classified: automatically display the alternative classification results of the remaining samples in the same initial group on the display 530; generate the final classification results of the remaining samples in the same initial group based on the final classification result of the sample to be classified; wherein, the initial grouping is generated based on the initial classification result of the sample, the sample to be classified is the sample to be reclassified, the reclassification is a verification or correction of the initial classification result, and the reclassification result obtained after the sample is reclassified is the final classification result.

[0074] In one embodiment of the present application, the peripheral area includes an area centered at the sample image and surrounding the sample image within a predetermined distance.

[0075] In one embodiment of the present application, the candidate classification results include at least two levels of classification results, the at least two levels of classification results are displayed level by level based on user instructions, and the final classification result of the sample to be classified is obtained based on at least one level of classification results.

[0076] In addition, according to an embodiment of the present application, a storage medium is further provided, on which program instructions are stored, and when the program instructions are run by a computer or a processor, the corresponding steps of the sample classification method of the embodiment of the present application are used to execute. The storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0077] In addition, according to an embodiment of the present application, a computer program is also provided, which can be stored on a cloud or local storage medium. When the computer program is executed by a computer or processor, it is used to perform the corresponding steps of the sample classification method of the embodiment of the present application.

[0078] Reference below Figure 6Describe an example of an application scenario of the sample classification method according to an embodiment of the present application. Figure 6 Figure 2 shows a schematic diagram of the structure of the film reading machine. Figure 6 As shown, the film reader is a medical device, which includes an objective lens 601 and a film reading platform 602 arranged relatively to each other, wherein a glass slide 603 is placed on the film reading platform 602, and a sample, such as a stained blood sample or body fluid sample, is dropped on the glass slide 603. The objective lens 601 is connected to a camera, so that the camera can capture the sample on the glass slide 603 through the objective lens 601 to obtain a sample image.

[0079] For the sample image, the film reader will output the initial classification result of the sample corresponding to the sample image on the human-computer interaction device (not shown) through automatic recognition of the sample image. The sample classification method according to the embodiment of the present application can be used to reclassify the initially classified samples. The user can call the sample classification method provided by the embodiment of the present application by operating the human-computer interaction device, and quickly and efficiently select the final classification result of the sample from the alternative classification results of the sample displayed in the peripheral area of the sample image, as described above.

[0080] Based on the above description, according to the sample classification method, device and storage medium of the embodiment of the present application, the alternative classification results of the sample to be classified are displayed in the surrounding area of the sample image of the sample to be classified, so that the user can quickly select the final classification result of the sample to be classified from the alternative classification results, thereby improving the efficiency of sample classification, and the user operation is simple, reducing the possibility of classification errors. In addition, according to the sample classification method, device and storage medium of the embodiment of the present application, the alternative classification results of the sample to be classified can also be displayed in a hierarchical manner, which can improve the efficiency of user information screening. Only when the previous level does not provide the target classification result, the next level is expanded, or the previous level provides guidance information for the next level, so that the user does not need to screen a large amount of information at the same time, thereby further improving the classification efficiency. In addition, according to the sample classification method, device and storage medium of the embodiment of the present application, the alternative classification results of the sample to be classified with different classification confidence levels can be displayed separately, which can further enhance the user's visual effect, so that the user can more clearly see the classification results with high classification confidence levels, which can not only further improve the classification efficiency, but also further reduce the possibility of classification errors due to such differentiated display.

[0081] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely illustrative and are not intended to limit the scope of the present application. Various changes and modifications may be made therein by those skilled in the art without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as required by the appended claims.

[0082] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another device, or ignoring or not performing some features.

[0084] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0085] Similarly, it should be understood that in order to streamline the present application and aid in understanding one or more of the various inventive aspects, in the description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this approach of the present application should not be interpreted as reflecting the intention that the application claimed for protection requires more features than those explicitly recited in each claim. More precisely, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with fewer features than all the features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim itself serving as a separate embodiment of the present application.

[0086] Those skilled in the art will understand that, except where mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus disclosed herein may be combined in any combination. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature providing the same, equivalent, or similar purpose.

[0087] Furthermore, those skilled in the art will appreciate that although some embodiments herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of this application and to form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

[0088] The various component embodiments of the present application can be implemented in hardware, or in a software module running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some modules according to the embodiments of the present application. The application can also be implemented as a part or all of a device program (e.g., a computer program and a computer program product) for performing the method described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0089] It should be noted that the above embodiments illustrate rather than limit the present application, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbols placed between brackets should not be construed as limiting the claims. The present application may be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

[0090] The above is merely a description of specific embodiments of the present application, and the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. The scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A cell sample classification method, characterized in that: The method comprises: Acquire a sample image of a sample to be classified, and display the sample image on a first display interface; receiving a classification instruction for reclassifying the sample to be classified corresponding to the sample image; Based on the classification instruction, alternative classification results of the sample to be classified are displayed in a peripheral area of the sample image for a user to select a final classification result of the sample to be classified, wherein the alternative classification results include a cell classification result, and the peripheral area includes an area centered on the sample image and surrounding the sample image within a predetermined distance; A user selection instruction for the candidate classification result is received, and a final classification result of the sample to be classified is obtained based on the selection instruction.

2. The method according to claim 1, characterized in that The candidate classification results are obtained based on automatic recognition of the sample images.

3. The method according to claim 1, characterized in that The candidate classification results include at least one classification result obtained by classifying the sample to be classified corresponding to the sample image, and each of the candidate classification results is sorted and displayed based on its own classification confidence.

4. The method according to claim 3, characterized in that The sorting display includes: the higher the classification confidence, the closer the display position of the corresponding classification result is to the sample image.

5. The method according to claim 3, characterized in that The candidate classification results include at least two levels of classification results, wherein the classification confidence of each of the first level classification results is higher than the classification confidence of each of the second level classification results, and the ranking display includes: The classification results of each level are displayed step by step based on user instructions, so as to obtain the final classification result of the sample to be classified based on at least one level of classification results.

6. The method according to any one of claims 3 to 5, characterized in that When displaying the candidate classification results or any level of classification results among the candidate classification results, the classification result with the highest classification confidence is displayed in a manner different from classification results with other classification confidences, or classification results with different classification confidences are displayed separately.

7. The method according to claim 6, characterized in that The differentiated display includes at least one of the following: The classification results of different classification confidence levels are displayed with icons of different colors; Display classification results of different classification confidence levels with icons of different sizes; Displaying classification results with different classification confidence levels using icons at different distances relative to the sample image; Display classification results of different classification confidence levels with text of different attributes; Display classification results with different classification confidence levels in regions with different shapes; The numerical value of the classification confidence of each classification result is displayed on the corresponding classification result.

8. The method according to claim 1, characterized in that The candidate classification results include at least two levels of classification results, wherein at least one level of classification results is divided into multiple groups, and the multiple groups include all possible classification results in the classification level. The classification items included in any group in the previous level of classification results are the next level of classification results. The display of the candidate classification results includes: The classification results of each level are displayed step by step based on user instructions, so as to obtain the final classification result of the sample to be classified based on at least one level of classification results.

9. The method according to claim 8, characterized in that The classification items included in each level of classification results in the at least two levels of classification results are displayed according to the level of classification confidence.

10. The method according to claim 5 or 8, characterized in that The latter level classification result is displayed in the surrounding area of the former level classification result.

11. The method according to claim 10, characterized in that The first level classification result among the at least two levels of classification results is displayed in at least a partial area surrounded by a first circle, and for the remaining levels of classification results among the at least two levels of classification results, the latter level classification results are displayed around the former level classification results in at least a partial area surrounded by a second circle, the first circle is concentric with the second circle and the radius of the second circle is larger than the radius of the first circle.

12. The method according to claim 5 or 8, characterized in that The previous level classification result is closer to the sample image than the next level classification result.

13. The method according to claim 1, wherein The candidate classification results are displayed on a second display interface, and the first display interface and the second display interface are different display interfaces.

14. The method according to claim 1, wherein The candidate classification results and the sample images are displayed in different states of the first display interface.

15. The method according to claim 1, wherein The classification instruction includes any one of the following instructions executed on the sample image: a drag instruction, a long press instruction, and a click instruction.

16. The method according to claim 1 or 2, characterized in that The classification instruction includes an instruction to input a classification keyword, and the candidate classification results include a classification result based on the classification keyword.

17. The method according to claim 5 or 8, characterized in that The user instruction includes the classification instruction and any one of the following instructions executed on any one of the previous level classification results: a hover instruction, a long press instruction, and a click instruction.

18. The method according to claim 1, wherein The method further comprises: Display the final classification results of the samples to be classified.

19. The method according to claim 3, characterized in that The candidate classification results displayed for the sample to be classified also include classification results obtained based on historical data, where the historical data includes final classification results of previous samples.

20. The method according to claim 19, characterized in that Compared with the classification results displayed based on the classification confidence ranking, the display position of the classification results obtained based on historical data is closer to the sample image; or, compared with the classification results obtained based on historical data, the display position of the classification results displayed based on the classification confidence ranking is closer to the sample image.

21. The method according to claim 18 or 19, characterized in that The method further comprises: After obtaining the final classification result of the sample to be classified, performing any one of the following operations on the remaining samples in the same initial group as the sample to be classified: Automatically display alternative classification results for the remaining samples in the same initial group; Generating final classification results of the remaining samples in the same initial group based on the final classification results of the samples to be classified; Among them, the initial grouping is generated based on the initial classification results of the samples, the samples to be classified are samples to be reclassified, the reclassification is a verification or correction of the initial classification results, and the reclassification results obtained after the samples are reclassified are the final classification results.

22. The method according to claim 1, wherein The candidate classification results include at least two levels of classification results, and the at least two levels of classification results are displayed level by level based on user instructions. The final classification result of the sample to be classified is obtained based on the at least one level of classification result.

23. A cell sample classification device, characterized in that: The device includes a memory, a processor, and a display, wherein the memory is used to store a program and a sample image of a sample to be classified, the display is used to display based on the control of the processor, and the processor is used to run the program in the memory to perform the following steps: Acquiring a sample image of the sample to be classified, and displaying the sample image on a first display interface by the display; receiving a classification instruction for reclassifying the sample to be classified corresponding to the sample image; Based on the classification instruction, alternative classification results of the sample to be classified are controlled to be displayed in a peripheral area of the sample image for a user to select a final classification result of the sample to be classified, wherein the alternative classification results include a cell classification result, and the peripheral area includes an area centered on the sample image and surrounding the sample image within a predetermined distance; A user selection instruction for the candidate classification result is received, and a final classification result of the sample to be classified is obtained based on the selection instruction.

24. The device according to claim 23, characterized in that The candidate classification result is obtained by the processor identifying the sample image.

25. The device according to claim 23, characterized in that The candidate classification results include at least one classification result obtained by classifying the sample to be classified corresponding to the sample image, and each of the candidate classification results is sorted and displayed based on its own classification confidence.

26. The device according to claim 25, characterized in that The sorting display includes: the higher the classification confidence, the closer the display position of the corresponding classification result is to the sample image.

27. The device according to claim 25, characterized in that The candidate classification results include at least two levels of classification results, wherein the classification confidence of each of the first level classification results is higher than the classification confidence of each of the second level classification results, and the ranking display includes: The classification results of each level are displayed step by step based on user instructions, so as to obtain the final classification result of the sample to be classified based on at least one level of classification results.

28. The device according to any one of claims 25 to 27, characterized in that When displaying the alternative classification results or any level of classification results among the alternative classification results on the display, the classification result with the highest classification confidence is displayed in a manner different from classification results with other classification confidences, or classification results with different classification confidences are displayed separately.

29. The device according to claim 28, characterized in that The differentiated display includes at least one of the following: The classification results of different classification confidence levels are displayed with icons of different colors; Display classification results of different classification confidence levels with icons of different sizes; Displaying classification results with different classification confidence levels using icons at different distances relative to the sample image; Display classification results of different classification confidence levels with text of different attributes; Display classification results with different classification confidence levels in regions with different shapes; The numerical value of the classification confidence of each classification result is displayed on the corresponding classification result.

30. The device according to claim 23, wherein The candidate classification results include at least two levels of classification results, wherein at least one level of classification results is divided into multiple groups, and the multiple groups include all possible classification results in the classification level, and the classification items included in any group in the previous level of classification results are the next level of classification results. The display of the candidate classification results includes: The classification results of each level are displayed step by step based on user instructions, so as to obtain the final classification result of the sample to be classified based on at least one level of classification results.

31. The device according to claim 30, characterized in that The classification items included in each level of classification results in the at least two levels of classification results are displayed according to the level of classification confidence.

32. The device according to claim 27 or 30, characterized in that The latter level classification result is displayed in the surrounding area of the former level classification result.

33. The device according to claim 32, characterized in that The first level classification result among the at least two levels of classification results is displayed in at least a partial area surrounded by a first circle, and for the remaining levels of classification results among the at least two levels of classification results, the latter level classification results are displayed around the former level classification results in at least a partial area surrounded by a second circle, the first circle is concentric with the second circle and the radius of the second circle is larger than the radius of the first circle.

34. The device according to claim 27 or 30, characterized in that The previous level classification result is closer to the sample image than the next level classification result.

35. The device according to claim 23, characterized in that The candidate classification results are displayed on a second display interface, and the first display interface and the second display interface are different display interfaces.

36. The device according to claim 23, characterized in that The candidate classification results and the sample images are displayed in different states of the first display interface.

37. The device according to claim 23, characterized in that The classification instruction includes any one of the following instructions executed on the sample image: a drag instruction, a long press instruction, and a click instruction.

38. The device according to claim 23 or 24, characterized in that The classification instruction includes an instruction to input a classification keyword, and the candidate classification results include a classification result based on the classification keyword.

39. The device according to claim 27 or 30, characterized in that The user instruction includes the classification instruction and any one of the following instructions executed on any one of the previous level classification results: a hover instruction, a long press instruction, and a click instruction.

40. The device according to claim 23, wherein The display is also used to: Display the final classification results of the samples to be classified.

41. The device according to claim 25, characterized in that The candidate classification results displayed for the sample to be classified also include classification results obtained based on historical data, where the historical data includes final classification results of previous samples.

42. The device according to claim 41, characterized in that Compared with the classification results displayed based on the classification confidence ranking, the display position of the classification results obtained based on historical data is closer to the sample image; or compared with the classification results obtained based on historical data, the display position of the classification results displayed based on the classification confidence ranking is closer to the sample image.

43. The device according to claim 40 or 41, characterized in that The processor is further configured to: After obtaining the final classification result of the sample to be classified, performing any one of the following operations on the remaining samples in the same initial group as the sample to be classified: Automatically displaying on the display the alternative classification results of the remaining samples in the same initial group; Generating final classification results of the remaining samples in the same initial group based on the final classification results of the samples to be classified; Among them, the initial grouping is generated based on the initial classification results of the samples, the samples to be classified are samples to be reclassified, the reclassification is a verification or correction of the initial classification results, and the reclassification results obtained after the samples are reclassified are the final classification results.

44. The device according to claim 23, characterized in that The candidate classification results include at least two levels of classification results, and the at least two levels of classification results are displayed level by level based on user instructions. The final classification result of the sample to be classified is obtained based on the at least one level of classification result.

45. A storage medium, characterized in that The storage medium stores a computer program, which, when run, executes the cell sample classification method according to any one of claims 1 to 22.

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