Lung Pathology Information Processing Method, Apparatus, Electronic Device, and Computer Medium

Abstract

Embodiments of the present disclosure disclose a method, apparatus, electronic device, and computer medium for processing pulmonary pathological information. A specific implementation of the method includes: controlling a pulmonary image acquisition terminal to acquire pulmonary image information of a user at a target position, where the pulmonary image information includes: a pulmonary image; extracting features from the pulmonary image included in the pulmonary image information; inputting the pulmonary image features into a pre-trained pulmonary image pathological recognition model; in response to determining that the pulmonary image pathological recognition result meets the abnormal detection condition, controlling an intelligent voice robot to conduct an intelligent Q&A with the user according to the pulmonary image pathological recognition result and collect the user's voice in real time; combining the user's user number, the pulmonary image pathological recognition result, and the pathological Q&A information into pulmonary pathological information, and sending the pulmonary pathological information to a doctor terminal. This implementation improves the detection efficiency of pulmonary images, can assist doctors in diagnosis, and speeds up the diagnosis efficiency of patients.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of pulmonary pathological information processing, and more particularly, to methods, devices, electronic devices, and computer media for processing pulmonary pathological information. Background Art

[0002] Currently, the number of patients with pulmonary complications is gradually increasing. For the diagnosis of pulmonary complications, it is usually necessary to first take a CT image, and then a doctor judges the patient's condition based on the image. However, the above-mentioned diagnosis method for pulmonary complications usually has the following technical problems: professional management personnel are required to operate the CT imaging machine, which affects the diagnosis efficiency of patients when there are many patients; in addition, the CT image is not pathologically analyzed, resulting in low doctor diagnosis efficiency and easy delay in the treatment time of patients.

[0003] The above information disclosed in this background art section is only used to enhance the understanding of the background of the inventive concept, and thus, it may include information that does not form the prior art known to those of ordinary skill in the art in this country. Summary of the Invention

[0004] The content part of the present disclosure is used to introduce the inventive concept in a brief form, and these inventive concepts will be described in detail in the following detailed implementation part. The content part of the present disclosure is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0005] Some embodiments of the present disclosure propose methods, devices, electronic devices, and computer-readable media for processing pulmonary pathological information to solve one or more of the technical problems mentioned in the above background art section.

[0006] In a first aspect, some embodiments of the present disclosure provide a method for processing pulmonary pathological information, which is applied to a pulmonary nodule integrated machine. The pulmonary nodule integrated machine includes: a pulmonary image acquisition terminal, an intelligent voice robot, and a doctor terminal. The method includes: in response to detecting a pulmonary image acquisition instruction, controlling the associated pulmonary image acquisition terminal to acquire the pulmonary image information of a user at a target position, where the pulmonary image information includes: pulmonary images; extracting features of the pulmonary images included in the pulmonary image information to obtain pulmonary image features; inputting the pulmonary image features into a pre-trained pulmonary image pathological recognition model to obtain a pulmonary image pathological recognition result; in response to determining that the pulmonary image pathological recognition result meets the abnormal detection condition, controlling the associated intelligent voice robot to conduct an intelligent Q&A with the user according to the pulmonary image pathological recognition result and to collect the voice of the user in real time; performing voice recognition on the voice of the user to obtain a voice recognition result, and filling the voice recognition result into a pre-set pathological Q&A information filling template to obtain pathological Q&A information; combining the user number of the user, the pulmonary image pathological recognition result, and the pathological Q&A information into pulmonary pathological information, and sending the pulmonary pathological information to the associated doctor terminal.

[0007] In a second aspect, some embodiments of the present disclosure provide a device for processing pulmonary pathological information, which is applied to a pulmonary nodule integrated machine. The pulmonary nodule integrated machine includes: a pulmonary image acquisition terminal, an intelligent voice robot, and a doctor terminal. The device includes: a first control unit configured to, in response to detecting a pulmonary image acquisition instruction, control the associated pulmonary image acquisition terminal to acquire the pulmonary image information of a user at a target position, where the pulmonary image information includes: pulmonary images; an extraction unit configured to extract features of the pulmonary images included in the pulmonary image information to obtain pulmonary image features; an input unit configured to input the pulmonary image features into a pre-trained pulmonary image pathological recognition model to obtain a pulmonary image pathological recognition result; a second control unit configured to, in response to determining that the pulmonary image pathological recognition result meets the abnormal detection condition, control the associated intelligent voice robot to conduct an intelligent Q&A with the user according to the pulmonary image pathological recognition result and to collect the voice of the user in real time; a recognition unit configured to perform voice recognition on the voice of the user to obtain a voice recognition result, and fill the voice recognition result into a pre-set pathological Q&A information filling template to obtain pathological Q&A information; a sending unit configured to combine the user number of the user, the pulmonary image pathological recognition result, and the pathological Q&A information into pulmonary pathological information, and send the pulmonary pathological information to the associated doctor terminal.

[0008] In a third aspect, some embodiments of the present disclosure provide an electronic device, including: one or more processors; a storage device storing one or more programs thereon, which, when executed by the one or more processors, cause the one or more processors to implement the method described in any implementation manner of the first aspect above.

[0009] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium storing a computer program thereon, wherein the program, when executed by a processor, implements the method described in any implementation manner of the first aspect above.

[0010] The above various embodiments of the present disclosure have the following beneficial effects: Through the lung pathological information processing method of some embodiments of the present disclosure, the detection efficiency of lung images is improved, thereby assisting doctors in diagnosis and accelerating the diagnosis efficiency of patients. Specifically, the reasons affecting the patient diagnosis efficiency and easily delaying the treatment opportunity of patients are as follows: Professional managers are required to operate the CT imaging machine, which affects the patient diagnosis efficiency when there are many patients; in addition, the CT images are not pathologically analyzed, resulting in low doctor diagnosis efficiency and easily delaying the treatment opportunity of patients. Based on this, the lung pathological information processing method of some embodiments of the present disclosure, first, in response to detecting a lung image acquisition instruction, controls the associated lung image acquisition end to acquire the lung image information of a user at a target position. Among them, the above lung image information includes: lung images. Feature extraction is performed on the lung images included in the above lung image information to obtain lung image features. Thus, it is convenient to perform pathological recognition on lung images. Secondly, the above lung image features are input into a pre-trained lung image pathological recognition model to obtain a lung image pathological recognition result. Thus, the lung images can be preliminarily pathologically detected through the pre-trained lung image pathological recognition model. In response to determining that the above lung image pathological recognition result meets the abnormal detection condition, controls the associated intelligent voice robot to conduct an intelligent Q&A with the above user according to the above lung image pathological recognition result and simultaneously collect the voice of the above user in real time. Thus, through the lung image pathological recognition model and the intelligent voice robot, the consultation on the patient's condition can be preliminarily completed. Thus, the consultation time of doctors is saved. Then, voice recognition is performed on the voice of the above user to obtain a voice recognition result, and the above voice recognition result is filled into a pre-set pathological Q&A information filling template to obtain pathological Q&A information; finally, the user number of the above user, the above lung image pathological recognition result, and the above pathological Q&A information are combined into lung pathological information, and the above lung pathological information is sent to the associated doctor terminal. Thus, the detection efficiency of lung images is improved, thereby assisting doctors in diagnosis and accelerating the diagnosis efficiency of patients. Description of the Drawings

[0011] In combination with the accompanying drawings and with reference to the following specific embodiments, the above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and the elements and elements are not necessarily drawn to scale.

[0012] Figure 1 is a flowchart of some embodiments of a method for processing pulmonary pathological information according to the present disclosure;

[0013] Figure 2 is a schematic structural diagram of some embodiments of a device for processing pulmonary pathological information according to the present disclosure;

[0014] Figure 3 is a schematic structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. Specific Embodiments

[0015] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not used to limit the protection scope of the present disclosure.

[0016] In addition, it should be noted that for the sake of convenience of description, only parts related to the relevant invention are shown in the drawings. Without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other.

[0017] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence relationship of the functions performed by these devices, modules or units.

[0018] It should be noted that the modifications of "one" and "plural" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly specified in the context, it should be understood as "one or more".

[0019] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0020] The present disclosure will be described in detail below with reference to the drawings and in combination with the embodiments.

[0021] Figure 1The flowchart of some embodiments of the method for processing pulmonary pathological information according to the present disclosure is shown. The process 100 of the method for processing pulmonary pathological information according to the present disclosure is shown. The method for processing pulmonary pathological information is applied to a pulmonary nodule integrated machine, and the pulmonary nodule integrated machine includes: a pulmonary image acquisition terminal, an intelligent voice robot, and a doctor terminal, and includes the following steps:

[0022] Step 101, in response to detecting a pulmonary image acquisition instruction, control the associated pulmonary image acquisition terminal to acquire the pulmonary image information of the user at the target position.

[0023] In some embodiments, the execution subject (for example, a computing device) of the method for processing pulmonary pathological information can, in response to detecting a pulmonary image acquisition instruction, control the associated pulmonary image acquisition terminal to acquire the pulmonary image information of the user at the target position. Among them, the above-mentioned pulmonary image information includes: pulmonary images. The pulmonary image acquisition instruction can refer to an instruction to acquire the pulmonary image of the user at the target position. For example, the pulmonary image acquisition terminal can be a pulmonary image acquisition CT. The target position can refer to the position of the user preset for acquiring the pulmonary image. The pulmonary image information can refer to the acquired pulmonary nodule image information (CT image) of the user.

[0024] Step 102, extract features from the pulmonary images included in the above-mentioned pulmonary image information to obtain pulmonary image features.

[0025] In some embodiments, the above-mentioned execution subject can extract features from the pulmonary images included in the above-mentioned pulmonary image information to obtain pulmonary image features. For example, the features can be extracted from the pulmonary images included in the above-mentioned pulmonary image information through a feature extraction model to obtain pulmonary image features. For example, the feature extraction model can refer to a convolutional neural network model.

[0026] In practice, the above-mentioned execution subject can extract features from the pulmonary images included in the above-mentioned pulmonary image information through the following steps:

[0027] The first step, perform color difference correction processing on the above-mentioned pulmonary images to obtain color difference corrected pulmonary images.

[0028] Among them, the above-mentioned first step can include the following sub-steps:

[0029] The first sub-step is to determine the image erasure area corresponding to the above-mentioned lung image. For example, the erasure area selected by the doctor for the above-mentioned lung image is determined as the image erasure area. The way for the doctor to select the erasure area can be to select it by moving a preset erasure tool. The preset erasure tool can be a planar object with a preset shape and size. For example, the preset erasure tool can be a circle with a diameter of a preset number of points. The image erasure area can be an area where the content within the area needs to be erased and the color is supplemented according to the image background.

[0030] The second sub-step is to generate a mask map and an initial erased image based on the above-mentioned image erasure area. A mask map with the same image size as the above-mentioned lung image can be generated, and the pixel values of the pixel points within the area corresponding to the above-mentioned image erasure area are 255, and the pixel values of the pixel points outside the area are 0. Then, the above-mentioned execution entity can use an image erasure algorithm to fill in the color within the above-mentioned image erasure area according to the lung image to obtain the initial erased image. For example, the image erasure algorithm can be the Stable Diffusion Inpainting algorithm.

[0031] The third sub-step is to generate a lung image integration map based on the above-mentioned lung image and the above-mentioned mask map. For example, for each pixel point in the mask area corresponding to the mask map in the above-mentioned lung image, the above-mentioned execution entity can replace the pixel value of the above-mentioned pixel point with [0, 0, 0]. Then, the lung image after replacing the pixel values of each pixel point within the mask area can be determined as the lung image integration map.

[0032] The fourth sub-step is to generate a lung image stitching tensor based on the above-mentioned initial erased image, the above-mentioned lung image integration map, and the above-mentioned mask map. For example, the above-mentioned execution entity can perform tensor conversion on the above-mentioned initial erased image, the above-mentioned lung image integration map, and the above-mentioned mask map respectively to obtain an initial color difference corrected lung image tensor, a lung image integration map tensor, and a mask map tensor. Then, the obtained initial color difference corrected lung image tensor, lung image integration map tensor, and mask map tensor can be stitched into a lung image stitching tensor.

[0033] The fifth sub-step is to input the above-mentioned lung image splicing tensor into a pre-trained lung image color difference correction model to obtain a color difference corrected lung image tensor. Among them, the above-mentioned lung image color difference correction model includes each encoding and decoding network with residual connections. In each of the above-mentioned encoding and decoding networks, the lung image splicing tensor of the input tensor and the output tensor of the previous encoding and decoding network is used as the input tensor of the next encoding and decoding network. The encoding and decoding network in each of the above-mentioned encoding and decoding networks includes a downsampling module and an upsampling module. The downsampling module includes a sequence of downsampling layers, and the upsampling module includes an upsampling layer sequence corresponding to the reverse order of the above-mentioned downsampling layer sequence. Each downsampling layer in the above-mentioned downsampling layer sequence is connected in sequence, and each upsampling layer in the above-mentioned upsampling layer sequence is connected in sequence. Moreover, each downsampling layer in the above-mentioned downsampling layer sequence is connected to the upsampling layer corresponding to the above-mentioned downsampling layer in the above-mentioned upsampling layer sequence. The output tensor of the previous downsampling layer in the above-mentioned downsampling layer sequence is used as the input tensor of the next downsampling layer, and the width and height of the input tensor of each downsampling layer are a preset multiple of the width and height of the output tensor; the lung image tensor corresponding to the output tensor of the previous upsampling layer in the above-mentioned upsampling layer sequence is used as the input tensor of the next upsampling layer, and the width and height of the output tensor of each upsampling layer are a preset multiple of the width and height of the input tensor. The above-mentioned lung image tensor is the lung image splicing tensor of the output tensor of the previous upsampling layer and the output tensor of the downsampling layer corresponding to the previous upsampling layer.

[0034] Thus, through each encoding and decoding network with residual connections, the fitting accuracy of the model can be improved. Also, because the integrated sample lung image can be used as an increased feature dimension of the model, and the model is composed of each encoding and decoding network with residual connections, it can improve the effect of solving color difference and boundary problems and the fitting accuracy of the model. Furthermore, it can reduce the color difference between the erased area and the non-erased area in the erased lung image.

[0035] The sixth sub-step is to convert the above-mentioned color difference corrected lung image tensor into a color difference corrected lung image corresponding to the initial erased image.

[0036] The second step is to extract features from the above-mentioned color difference corrected lung image to obtain lung image features. For example, the above-mentioned color difference corrected lung image can be used to extract lung image features through a feature extraction model. For example, the feature extraction model can be a bert model.

[0037] Optionally, the lung image color difference correction model can be trained through the following steps:

[0038] The first step is to obtain a set of lung image samples. Among them, the lung image samples in the above set of lung image samples include sample chromatic aberration lung images, sample lung image mask maps, and sample non-chromatic aberration lung images. The sample chromatic aberration lung image can be a lung image with a chromatic aberration between the mask area and other areas. The sample lung image mask map can be a mask map containing a mask area. The mask map can be a grayscale image. The pixel value of the pixel points within the mask area in the mask map can be 255. The pixel value of the pixel points outside the mask area in the mask map can be 0. The sample non-chromatic aberration lung image can be a lung image without a chromatic aberration between the mask area and other areas.

[0039] The second step is to perform the following processing steps for each lung image sample in the above set of lung image samples:

[0040] First, according to the sample chromatic aberration lung image, the sample lung image mask map, and the sample non-chromatic aberration lung image included in the above lung image sample, a sample lung image integration map is generated. For each pixel point within the mask area corresponding to the sample lung image mask map in the above sample chromatic aberration lung image, the execution entity can replace the pixel value of the above pixel point with [0, 0, 0]. Then, the sample chromatic aberration lung image after replacing the pixel values of each pixel point within the mask area can be determined as the sample lung image integration map.

[0041] Next, based on the above sample chromatic aberration lung image, the above sample lung image mask map, and the above sample lung image integration map, a spliced lung image tensor is generated. First, the execution entity can perform tensor conversion on the above sample chromatic aberration lung image, the above sample lung image mask map, and the above sample lung image integration map respectively to obtain a sample chromatic aberration lung image tensor, a sample lung image mask map tensor, and a sample lung image integration map tensor. Then, the above sample chromatic aberration lung image tensor, the above sample lung image mask map tensor, and the above sample lung image integration map tensor can be spliced into a spliced lung image tensor.

[0042] After that, the above sample non-chromatic aberration lung image is converted into a sample non-chromatic aberration lung image tensor. The above sample non-chromatic aberration lung image can be subjected to tensor conversion to obtain a sample non-chromatic aberration lung image tensor.

[0043] Then, the above spliced lung image tensor and the above sample non-chromatic aberration lung image tensor are combined into a lung image training sample.

[0044] The third step is to train the initial lung image chromatic aberration correction model according to each lung image training sample to obtain a trained lung image chromatic aberration correction model. The lung image chromatic aberration correction model can be trained in a distributed training manner.

[0045] Among them, the above third step can include the following sub-steps:

[0046] The first sub-step is to select a target lung image training sample from each of the above lung image training samples. One lung image training sample can be randomly selected from each of the lung image training samples as the target lung image training sample.

[0047] The second sub-step is to input the spliced lung image tensor included in the target lung image training sample into the initial lung image color difference correction model to obtain a color difference-free lung image tensor.

[0048] The third sub-step is to determine the loss value between the above color difference-free lung image tensor and the sample color difference-free lung image tensor corresponding to the above target lung image training sample. For example, the loss value between the above color difference-free lung image tensor and the sample color difference-free lung image tensor corresponding to the above target lung image training sample can be determined by a cross-entropy loss function or a hinge loss function.

[0049] The fourth sub-step is to determine whether the above loss value is less than or equal to a preset loss value.

[0050] The fifth sub-step is to, in response to determining that the above loss value is less than or equal to the preset loss value, determine the above initial lung image color difference correction model as the trained lung image color difference correction model.

[0051] Step 103: Input the above lung image features into a pre-trained lung image pathological recognition model to obtain a lung image pathological recognition result.

[0052] In some embodiments, the above execution subject can input the above lung image features into a pre-trained lung image pathological recognition model to obtain a lung image pathological recognition result. The lung image pathological recognition model can refer to a pre-trained neural network model that takes lung image features as input and outputs lung image pathological recognition results. For example, the lung image pathological recognition model can be a multi-scale capsule-weighted fusion classification network (MCFCN). The lung image pathological recognition result can indicate whether there are lung nodule features in the lung image.

[0053] Among them, the lung image pathological recognition model can be trained through the following steps:

[0054] The first step is to obtain a lung image feature sample set. Among them, the lung image feature samples in the above lung image feature sample set include sample lung features and sample labels. The sample label can indicate whether the sample lung feature is a lung nodule feature.

[0055] Step 2: Select target lung image feature samples from the above lung image feature sample set.

[0056] Step 3: Input the sample lung image features included in the above target lung image feature samples into the initial lung image pathological recognition model to obtain an initial lung image pathological recognition result.

[0057] Step 4: Determine the model loss value between the above initial lung image pathological recognition result and the corresponding sample label. The model loss value between the above initial lung image pathological recognition result and the corresponding sample label can be determined by a preset loss function.

[0058] Step 5: In response to determining that the above model loss value is less than or equal to the preset model loss value, determine the initial lung image pathological recognition model as the trained lung image pathological recognition model.

[0059] Thus, the pathological detection and recognition of lung images can be performed through a pre-trained lung image pathological recognition model. Thereby, the diagnosis efficiency of doctors is improved.

[0060] Step 104: In response to determining that the above lung image pathological recognition result meets the abnormal detection condition, control the associated intelligent voice robot to conduct an intelligent Q&A with the user based on the above lung image pathological recognition result and collect the user's voice in real time.

[0061] In some embodiments, the above execution subject can, in response to determining that the above lung image pathological recognition result meets the abnormal detection condition, control the associated intelligent voice robot to conduct an intelligent Q&A with the user based on the above lung image pathological recognition result and collect the user's voice in real time. The abnormal detection condition may be that the lung image pathological recognition result indicates the presence of lung nodules in the lung. The intelligent voice robot may be an intelligent robot with an intelligent Q&A function, which can conduct an intelligent Q&A with the user to inquire about the user's physical condition. Collecting the user's voice in real time may refer to the user's reply voice to the questions raised by the intelligent voice robot.

[0062] Step 105: Perform speech recognition on the above user's voice to obtain a speech recognition result, and fill the speech recognition result into a preset pathological Q&A information filling template to obtain pathological Q&A information.

[0063] In some embodiments, the above-mentioned execution entity may perform speech recognition on the speech of the above-mentioned user to obtain a speech recognition result, and fill the speech recognition result into a pre-set pathological Q&A information filling template to obtain pathological Q&A information. For example, the speech of the above-mentioned user may be recognized by speech-to-text software to obtain a speech recognition result. The pathological Q&A information filling template may be a pre-constructed template for filling in reply information about lung patients. For example, the pathological Q&A information filling template may include various field types. The text corresponding to the field type in the speech recognition result may be filled in the blank filling place corresponding to the field type. For example, the pathological Q&A information filling template may be "Name: [ ]; Age: [ ]; Symptom description: [ ]". That is, "[ ]" may represent a blank filling place.

[0064] Step 106, combine the user ID of the above-mentioned user, the above-mentioned lung image pathological recognition result and the above-mentioned pathological Q&A information into lung pathological information, and send the lung pathological information to the associated doctor terminal.

[0065] In some embodiments, the above-mentioned execution entity may combine the user ID of the above-mentioned user, the above-mentioned lung image pathological recognition result and the above-mentioned pathological Q&A information into lung pathological information, and send the lung pathological information to the associated doctor terminal. The doctor terminal may be an operation terminal of a lung doctor who analyzes lung pathological information.

[0066] Further reference Figure 2 , as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a lung pathological information processing device. These embodiments of the lung pathological information processing device correspond to Figure 1 the method embodiments shown, and the lung pathological information processing device may be specifically applied to various electronic devices.

[0067] As Figure 2As shown in the figure, the pulmonary pathological information processing device 200 of some embodiments includes: a first control unit 201, an extraction unit 202, an input unit 203, a second control unit 204, an identification unit 205, and a sending unit 206. Among them, the first control unit 201 is configured to control the associated pulmonary image acquisition end to acquire the pulmonary image information of the user at the target position in response to detecting a pulmonary image acquisition instruction, where the above-mentioned pulmonary image information includes: pulmonary images; the extraction unit 202 is configured to extract features from the pulmonary images included in the above-mentioned pulmonary image information to obtain pulmonary image features; the input unit 203 is configured to input the above-mentioned pulmonary image features into a pre-trained pulmonary image pathological identification model to obtain a pulmonary image pathological identification result; the second control unit 204 is configured to control the associated intelligent voice robot to conduct an intelligent Q&A with the above-mentioned user based on the above-mentioned pulmonary image pathological identification result and to collect the voice of the above-mentioned user in real time in response to determining that the above-mentioned pulmonary image pathological identification result meets the abnormal detection condition; the identification unit 205 is configured to perform voice recognition on the voice of the above-mentioned user to obtain a voice recognition result, and to fill the above-mentioned voice recognition result into a pre-set pathological Q&A information filling template to obtain pathological Q&A information; the sending unit 206 is configured to combine the user number of the above-mentioned user, the above-mentioned pulmonary image pathological identification result, and the above-mentioned pathological Q&A information into pulmonary pathological information, and to send the above-mentioned pulmonary pathological information to the associated doctor terminal.

[0068] It can be understood that the various units described in the pulmonary pathological information processing device 200 correspond to the respective steps in the method described with reference to Figure 1 Therefore, the operations, features, and beneficial effects described above for the method also apply to the pulmonary pathological information processing device 200 and the units included therein, and will not be elaborated here.

[0069] Next, with reference to Figure 3 , which shows a schematic structural diagram of an electronic device 300 (for example, a computing device) suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is only an example and should not impose any limitations on the functions and usage scopes of the embodiments of the present disclosure.

[0070] As Figure 3As shown, the electronic device 300 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 301, which may perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage device 308 into the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0071] Generally, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 3 the electronic device 300 with various devices is shown, it should be understood that it is not required to implement or include all the shown devices. More or fewer devices may be implemented or included alternatively. Figure 3 Each block shown in may represent a device or, as needed, multiple devices.

[0072] Specifically, according to some embodiments of the present disclosure, the process described above with reference to the flowchart may be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the method shown in the flowchart. In such some embodiments, the computer program may be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above functions defined in the method of some embodiments of the present disclosure are executed.

[0073] It should be noted that the computer-readable media described in some embodiments of the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable signal medium may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0074] In some embodiments, the client and the server may communicate using any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0075] The above computer-readable medium may be included in the above electronic device; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device is caused to: in response to detecting a lung image acquisition instruction, control an associated lung image acquisition end to acquire lung image information of a user at a target position, wherein the lung image information includes: a lung image; extract features of the lung image included in the lung image information to obtain lung image features; input the lung image features into a pre-trained lung image pathology recognition model to obtain a lung image pathology recognition result; in response to determining that the lung image pathology recognition result meets an anomaly detection condition, control an associated intelligent voice robot to conduct an intelligent Q&A with the user according to the lung image pathology recognition result and to collect the voice of the user in real time; perform speech recognition on the voice of the user to obtain a speech recognition result, and fill the speech recognition result into a pre-set pathology Q&A information filling template to obtain pathology Q&A information; combine the user number of the user, the lung image pathology recognition result and the pathology Q&A information into lung pathology information, and send the lung pathology information to an associated doctor terminal.

[0076] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++; and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., by connecting through an Internet service provider using the Internet).

[0077] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0078] The units described in some embodiments of the present disclosure can be implemented in software or in hardware. The described units can also be provided in a processor. For example, it can be described as: a processor includes: a first control unit, an extraction unit, an input unit, a second control unit, an identification unit, and a transmission unit. Among them, the names of these units do not constitute a limitation on the unit itself in some cases. For example, the identification unit can also be described as "a unit that performs speech recognition on the speech of the above user to obtain a speech recognition result, and fills the speech recognition result into a pre-set pathological Q&A information filling template to obtain pathological Q&A information".

[0079] The functions described above can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), Application Specific Standard Product (ASSP), System on Chip (SOC), Complex Programmable Logic Device (CPLD), and so on.

[0080] The above description is only some preferred embodiments of the present disclosure and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, technical solutions formed by mutually replacing the above features with technical features (but not limited to) having similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A lung pathology information processing method, applied to a lung nodule all-in-one machine, the lung nodule all-in-one machine comprising: Lung image acquisition end, intelligent voice robot and doctor end, including: In response to detecting a lung image acquisition instruction, controlling the associated lung image acquisition terminal to acquire lung image information of the user at the target position, wherein the lung image information includes: lung images; Determine an image erasing area corresponding to the lung image; Based on the image erasing area, generating a mask map and an initial erasing image; Generate a lung image integration map based on the lung image and the mask map; generating a lung image splicing tensor based on the initial erased image, the lung image integration map and the mask map; Inputting the lung image stitching tensor into a pre-trained lung image chromatic aberration correction model to obtain a chromatic aberration corrected lung image tensor; converting the chromatic aberration-corrected lung image tensor into a chromatic aberration-corrected lung image corresponding to the initial erased image; Performing feature extraction on the chromatic aberration-corrected lung image to obtain lung image features; Inputting the lung image features into a pre-trained lung image pathology recognition model to obtain a lung image pathology recognition result; In response to determining that the lung image pathology recognition result meets the abnormality detection condition, controlling the associated intelligent voice robot to conduct intelligent question and answer with the user according to the lung image pathology recognition result, and collecting the user's voice in real time; Performing speech recognition on the user's speech to obtain a speech recognition result, and filling the speech recognition result into a preset pathology question and answer information filling template to obtain pathology question and answer information; The user number of the user, the lung image pathology recognition result and the pathology question and answer information are combined into lung pathology information, and the lung pathology information is sent to an associated doctor terminal.

2. The method according to claim 1, wherein: Before inputting the lung image features into a pre-trained lung image pathology recognition model to obtain a lung image pathology recognition result, the method further includes: Acquire a lung image feature sample set, wherein the lung image feature samples in the lung image feature sample set include sample lung features and sample labels; Selecting a target lung image feature sample from the lung image feature sample set; Inputting the sample lung image features included in the target lung image feature sample into the initial lung image pathology recognition model to obtain an initial lung image pathology recognition result; Determining a model loss value between the initial lung image pathology recognition result and the corresponding sample label; In response to determining that the model loss value is less than or equal to the preset model loss value, the initial lung image pathology recognition model is determined as the trained lung image pathology recognition model.

3. A pulmonary pathology information processing device, applied to a pulmonary nodule integrated machine, the pulmonary nodule integrated machine comprising: Lung image acquisition end, intelligent voice robot and doctor end, including: The first control unit is configured to control the associated lung image acquisition terminal to acquire lung image information of the user at the target position in response to detecting the lung image acquisition instruction, wherein the lung image information includes: lung image; The extraction unit is configured to determine an image erasure area corresponding to the lung image; generate a mask image and an initial erased image based on the image erasure area; generate a lung image integration image based on the lung image and the mask image; generate a lung image splicing tensor based on the initial erased image, the lung image integration image and the mask image; input the lung image splicing tensor into a pre-trained lung image chromatic aberration correction model to obtain a chromatic aberration corrected lung image tensor; convert the chromatic aberration corrected lung image tensor into a chromatic aberration corrected lung image corresponding to the initial erased image; perform feature extraction on the chromatic aberration corrected lung image to obtain lung image features; An input unit is configured to input the lung image features into a pre-trained lung image pathology recognition model to obtain a lung image pathology recognition result; A second control unit is configured to, in response to determining that the lung image pathology recognition result meets the abnormality detection condition, control the associated intelligent voice robot to conduct intelligent question and answer with the user according to the lung image pathology recognition result, and collect the user's voice in real time; A recognition unit configured to perform speech recognition on the user's speech to obtain a speech recognition result, and fill the speech recognition result into a preset pathology question and answer information filling template to obtain pathology question and answer information; The sending unit is configured to combine the user number of the user, the lung image pathology recognition result and the pathology question and answer information into lung pathology information, and send the lung pathology information to the associated doctor end.

4. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-2.

5. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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