A method and system for screening typical areas of ultra-large images

Abstract

This invention proposes a method and system for screening typical regions in ultra-large images. The method comprises the following steps: obtaining a full-field pathology image; inputting the full-field pathology image into a configurable hierarchical sampling network, which performs hierarchical sampling on the full-field pathology image to screen for typical regions; the configurable hierarchical sampling network comprises a cascade of multiple sampling modules; the j-th sampling module performs typical region screening on the low-resolution sub-image blocks output by the previous sampling module, and maps these onto the full-field pathology image at its original resolution, obtaining multiple pathology image blocks at the original resolution with the same range as the currently screened regions. This method can effectively process extremely high-resolution full-field pathology images and screen for typical regions.

Description

Technical Field

[0001] The present invention relates to the technical field of computer image processing, and in particular to a method and system for screening typical areas of an ultra-large image. Background Art

[0002] Ovarian cancer is one of the most lethal cancers of the female reproductive system. High-grade serous adenocarcinomas (HGSA) of the ovary account for 75% of ovarian cancer cases and 80-90% of deaths, and have the worst prognosis of all ovarian cancer subtypes. Even with the same treatment, the prognosis for patients with advanced-stage HGSA ovarian cancer varies significantly. While a small percentage of patients experience short-term recurrence after treatment and enjoy a long survival period, the majority (>75%) experience recurrence within two years.

[0003] The prognosis prediction of ovarian HGSA has important clinical value. Therefore, with the advancement of science and technology in recent years, technicians in this field have added artificial intelligence algorithms to perform prognosis prediction.

[0004] The application of artificial intelligence algorithms in ovarian HGSA analysis is primarily focused on bioinformatics mining. In recent years, deep learning methods, which can automatically learn from images and rapidly obtain superior feature representations, have achieved significant success in computer vision. Simultaneously, advances in medical imaging and data storage technologies have led to widespread application of deep learning in computer-aided diagnosis. However, despite the significant potential of deep learning, its application in pathological image analysis faces significant challenges.

[0005] Due to the high resolution of full-field pathology images, one difficulty in using computer-assisted analysis in this field is that full-field tissue pathology images with hundreds of millions or even billions of pixels cannot be directly input into existing deep learning networks for training and reasoning. Summary of the Invention

[0006] In order to overcome the above-mentioned defects in the prior art, the purpose of the present invention is to provide a method and system for screening typical areas of a super-large image.

[0007] In order to achieve the above-mentioned object of the present invention, the present invention provides a method for screening typical regions of a large image, comprising the following steps:

[0008] Acquire full-field pathology images;

[0009] The pathological full-field image is input into a configurable hierarchical sampling network, and the configurable hierarchical sampling network is used to perform hierarchical sampling on the pathological full-field image to screen out typical areas.

[0010] The configurable hierarchical sampling network includes cascaded multi-stage sampling modules;

[0011] The first-level sampling module screens typical regions on the low-resolution image of the pathological full-field image and maps the regions onto the original-resolution pathological full-field image, thereby obtaining a plurality of original-resolution pathological image blocks with the same range as the screened regions, i.e., first-level sub-image blocks;

[0012] The j-th level sampling module performs typical area screening on the low-resolution image of the sub-image block output by the sampling module of the previous level and maps it to the pathological full-field image of the original resolution, obtaining multiple pathological image blocks of the original resolution with the same area range as the currently screened area, namely, the j-th level sub-image block, where j is an integer greater than 1.

[0013] This method can effectively process huge resolution full-field pathology images and screen out typical areas. The configurable layered sampling network emphasizes the ability to process a small number of full-resolution image blocks of the input image, which can significantly reduce peak GPU memory usage and have higher sampling accuracy. For extremely large images, such as mega- to gigapixel images, the number of selected image blocks is much smaller than the size of the sample space.

[0014] In an optional solution of the method for screening typical regions of a very large image, each level of the sampling module includes a feature extraction unit, an attention weighting unit, and a classification unit;

[0015] The feature extraction unit extracts features from the pathological full-field image or the sub-image block output by the previous sampling module, and the classification unit classifies the features extracted by the feature extraction unit based on the weights of the attention weighting unit to obtain sub-image blocks.

[0016] This optional solution achieves the effect of directly using deep neural networks to screen typical feature areas from ultra-large images with hundreds of millions or even billions of pixels (for example, full-field tissue pathology images).

[0017] In an optional solution of the typical region screening method for a very large image, the function of the j-th level sampling module is:

[0018] in, is the classification function of the j-th level sampling module, is the feature extraction function of the j-th level sampling module, is the attention function of the j-th level sampling module, c (j) is the input image x of the j-th level sampling module j The ratio is S j The low-resolution sampling of ∈(0,1) corresponds to the coordinate mapping of the pathological full-field image, and C is the coordinate mapping of all c (j) The set of components;

[0019] j-1th level sampling function is the input image x to the j-1th level sampling module j-1 The ratio is S j-1 ∈(0,1) low-resolution sampling to obtain coordinates c (j-1) And c (j-1) Mapped to the j-1th level sampling module input image x j-1 middle, is the j-th level sampling function, the input image x of the j-th level sampling module j for

[0020] This optional solution adopts a multi-layer step-by-step sampling mode, so that the sampling module of each layer only processes the area output by the previous layer, without processing other areas; through the coordinate c (j-1) And the scaling ratio can ensure that the output of the sampling module at each layer can be mapped back to the original image (pathological full-field image).

[0021] In an optional solution of the typical region screening method for a very large image, after Monte Carlo approximation, the function of the j-th level sampling module is: Among them, M refers to the number of sub-image blocks output by each sampling module, c (j-1) ∈C.

[0022] This alternative can effectively avoid j ,c) calculate features in all image blocks to improve the operation speed.

[0023] In an optional solution of the method for screening typical regions of large images, the loss function of the configurable layered sampling network is:

[0024] in, is the weighted sum of the cross entropy of sampling modules at all levels, is the cross entropy of the j-th level sampling module, α (j) is the weight of the j-th level sampling module,

[0025] L RZ =max{||CMLS-SAP1-AM(·)||2,||CMLS-SAP2-AM(·)||2,...,||CMLS-SAP N -AM(·)||2},||CMLS-SAP j -AM(·)||2 is the second-order norm of the network weight of the attention unit of the j-th sampling module, β is the weight coefficient of the regularization term, and N is the total number of sampling modules.

[0026] In this optional solution, a regularization term is introduced into the loss function, which allows the model to keep the model simple while minimizing the training error, while improving the model's generalization performance and preventing overfitting.

[0027] In an alternative solution of the method for screening typical regions of a large image, the weight ω1 (j) is the area ratio or quantity ratio of the output image blocks of all levels of sampling modules to the output image blocks of the jth level, ω2 (j) It is the difference between the ratio of the sum of the gradient values ​​of the image block with the smallest sum of gradient information and the image block with the largest sum of gradient information among all image blocks output by the j-th level sampling module and 1,

[0028] Among them, M (j) is the total number of output image blocks of the j-th level sampling module,

[0029] Among them, tile (j) is the gradient information in the image block output by the j-th level sampling module, and m is the sequence number of the image block output by the j-th level sampling module.

[0030] The smaller it is, the larger the weight is given to it; the ratio of the number of image blocks output by the l-th level sampling module to the sum of the number of image blocks output by all sampling modules can be directly used to achieve fast approximate calculation. The larger the value is, the higher the degree of information consistency of the output sampled image block is, and a larger weight is given to this part of the output.

[0031] The present invention also proposes a system for screening typical areas of very large images, including an image receiving module, a processing module and a storage module. The image receiving module receives images for training or evaluation and sends the received images to the processing module. The processing module is communicatively connected to the storage module. The storage module is used to store at least one executable instruction. The executable instruction enables the processing module to perform operations corresponding to the above-mentioned method for screening typical areas of very large images based on the images it receives.

[0032] The beneficial effects of the present invention are:

[0033] The present invention can effectively process pathology full-field-of-view images, use low-resolution views to indicate key information areas in the reduced view for the purpose of prognostic analysis, and then extract key features in the high-resolution view (pathology full-field-of-view image) based on the key information areas, without the need to obtain the entire pathology full-field-of-view image.

[0034] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments with reference to the following drawings, in which:

[0036] Figure 1 It is a schematic diagram of the structure of the configurable hierarchical sampling network;

[0037] Figure 2 It is a structural diagram of the j-th level sampling module in the configurable hierarchical sampling network. DETAILED DESCRIPTION

[0038] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0039] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal communication between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0040] Example 1

[0041] The present invention provides a method for screening typical regions of a large image, which specifically includes the following steps:

[0042] Acquire full-field pathology images (reaching billions or even billions of pixels, hence the name ultra-large images).

[0043] In this example, data from patients with ovarian HGSA were collected as samples. Each patient data set included a full-field pathological image of the primary lesion of the ovarian tumor tissue.

[0044] The pathological full-field image is input into a configurable hierarchical sampling network CMLS that has been pre-trained according to the screening requirements. The configurable hierarchical sampling network CMLS is used to perform hierarchical sampling on the pathological full-field image to screen out typical areas.

[0045] The configurable layered sampling network CMLS includes cascaded multi-stage sampling modules. In this embodiment, the structure of the configurable layered sampling network CMLS is as follows: Figure 1As shown, it includes the first-level sampling module CMLS-SAP1(·), the second-level sampling module CMLS-SAP2(·), and the N-level sampling module CMLS-SAP n The number N of the sampling modules is configurable. In this embodiment, the value range of N is 1-5, preferably 2 or 3.

[0046] The first-level sampling module CMLS-SAP1(·) screens typical regions on the low-resolution pathology full-field image and maps them onto the original-resolution pathology full-field image, obtaining TN1 first-level sub-image blocks of the original resolution with the same range as the screened regions.

[0047] The sampling modules at each level are cascaded in sequence and perform typical area screening on the low-resolution image of the sub-image block output by the sampling module at the previous level and map it to the pathological full-field image at the original resolution, thereby obtaining multiple sub-image blocks at each level with the same area range as the currently screened area.

[0048] That is, the second-level sampling module CMLS-SAP2(·) screens typical regions on the low-resolution image of TN1 first-level sub-image blocks and maps them onto the full-field pathology image of the original resolution, obtaining TN2 second-level sub-image blocks of the original resolution with the same regional range as the currently screened region;

[0049] Sequentially, the j-th level sampling module CMLS-SAP j (·) Perform typical area screening on the low-resolution sub-image blocks output by the previous sampling module and map them onto the full-field pathology image at the original resolution to obtain multiple j-th sub-image blocks at the original resolution with the same area range as the currently screened area, where j is an integer greater than 1.

[0050] Until the Nth level sampling module CMLS-SAP n (·) In TN n-1 The typical area is screened on the low-resolution image of the (N-1)th level sub-image block and mapped to the original resolution pathological full-field image to obtain the original resolution TN with the same regional range as the current screened area. N N-th level sub-image blocks, which contain image detail information for subsequent analysis. The low resolutions collected by different sampling modules in the configurable layered sampling network CMLS can be the same or different, and are all between 0 and 1, preferably but not limited to increasing step by step. It emphasizes the ability to process a small number of full-resolution image blocks of the input image, which can significantly reduce peak GPU memory usage and have higher sampling accuracy. For very large images (such as mega-pixels to gigapixels), the number of selected image blocks is much smaller than the sample space || S sampling The size of ||.

[0051] like Figure 2 As shown, each level of the sampling module of the configurable layered sampling network CMLS includes a feature extraction unit CMLS-SAP j -FE(·), attention weighted unit CMLS-SAP j -AM(·) and taxon CMLS-SAP j -CF(·), (1≤j≤N). Feature extraction unit CMLS-SAP j -FE(·) extracts features from the full-field pathology image or the sub-image block output by the previous sampling module, and the classification unit CMLS-SAP j -CF(·) is based on the attention weighted unit CMLS-SAP j The weight of -AM(·) is used to classify the features extracted by the feature extraction unit to obtain sub-image blocks.

[0052] In this embodiment, the feature extraction unit CMLS-SAP j -FE(·) is composed of trainable parameters A typical example of a deep convolutional neural network for characterization is shown in Table 1.

[0053] Attention Weighted Unit CMLS-SAP j -AM(·) is composed of trainable parameters A smaller deep convolutional neural network represented by

[15] is used to obtain the attention weight matrix, as shown in Table 2, a typical example of which.

[0054] Taxonomic unit CMLS-SAP j -CF(·) is composed of trainable parameters A single fully connected layer fc-n representing class .

[0055] Table 1 Feature extraction unit CMLS-SAP j A typical example of -FE(·)

[0056] layer type 1 Conv(3,1,1,8)+ReLU() 2 Conv(3,1,1,16)+ReLU() 3 Conv(3,1,1,32)+ReLU() 4 Conv(3,1,1,16)+ReLU() 5 Conv(3,1,1,8)+ReLU() 6 Conv(3,1,1,16)+ReLU() 7 Conv(3,1,1,32)+ReLU() 8 Conv(3,1,1,1)+ReLU() 9 GlobalAveragePooling2D() 10 SoftMax()

[0057] Table 2 Attention weighted unit CMLS-SAP j A typical example of -AM(·)

[0058] layer type 1 Conv(3,1,1,8)+ReLU() 2 Conv(3,1,1,16)+ReLU() 3 Conv(3,1,1,32)+ReLU() 4 Conv(3,1,1,1)+SoftMax() 5 SoftMax()

[0059] The action function CMLS-SAP1(x) of the first level (i.e., when j=1) sampling module of the configurable layered sampling network CMLS is as follows, which is given by θ={θ f ,θ a ,θ c}Parameterized convolutional neural network:

[0060]

[0061] in, is the classification function of the classification unit of the first-level sampling module, It is the feature extraction function of the feature extraction unit of the first-level sampling module, and the sampling function From the input image x1∈R H×W Extract the first-level sub-image block of size h1×w1, which corresponds to the low-resolution view V(x,s1)∈R with a scale of s1∈(0,1) h×w The coordinates c1 = {i, j} in the sampling function, c1 represents the corresponding coordinates of the thumbnail view processed by the first-level sampling module (the low-resolution view after the original image (the pathological full-field image) is reduced) and the original image (the pathological full-field image), (i, j) is its horizontal and vertical coordinates in the original image, and the sampling function pass Maps coordinate c1 to a location in image x1 and returns a subimage patch of size h1×w1, where H×W is the resolution of image x1. and h is the height of the thumbnail view of the input 1st level sampling module, w is the width of the thumbnail view of the input 1st level sampling module, It is a floor operation.

[0062] The attention mechanism is introduced in CMLS-SAP1(x), and the convolutional neural network of the attention mechanism is α=a θ (V(x,s1)), where a θ (·) represents the trainable parameters The smaller deep convolutional neural network represented is used to obtain the attention weight matrix and obtain the attention weighted network:

[0063] Among them, g Θ Generally refers to the classification function in the sampling module, here refers to the classification function in the l-th level sampling module f Θ Generally refers to the feature extraction function in the sampling module, here refers to the feature extraction function in the l-th layer sampling module is the attention matrix, c = (i, j) represents the corresponding coordinates of the thumbnail view processed by the sampling module of this layer and the original image (pathological full-field image), (i, j) represents its horizontal and vertical coordinates in the original image, C is the set of c in the sampling modules of each layer, α c Refers to the convolutional neural network with attention mechanism α=a θ (V(x,s1,c)),||S sampling|| is the set of all index pairs of the low-resolution view V(x,s1) of image x1, ||S sampling ||=h×w.

[0064] In order to avoid computing features from all sub-image patches of the low-resolution view V(x,s1), the formula Perform Monte Carlo approximation to obtain the function of the first-level sampling module: Where C={(i,j)~a θ (V(x, s1))|i=1,2,...,M} is the index pair M<<||S of the low-resolution view V(x, s1) sampling CMLS-SAP1(x) uses the low-resolution view V(x,s1) of image x1 to calculate the attention weight, obtains M sub-image blocks by sampling, and The average is used to obtain the aggregated features, P refers to the total number of image categories, z n Refers to the unique features of the nth image category.

[0065] Similarly, the action function of the j-th level sampling module of the configurable layered sampling network CMLS is is given by θ={θ f ,θ a ,θ c}Parameterized convolutional neural network.

[0066] in, is the classification function of the classification unit of the j-th level sampling module, is the feature extraction function of the feature extraction unit of the j-th level sampling module, View V(x,s j ,c)∈R u×v is the sampling function Based on scale S j ∈(0,1), c can map the thumbnail view of the layer back to the original image (pathological full field image). CMLS-SAP j (x) introduces the view V(x,s j ,c) Attention mechanism CMLS-SAP j -AM(·),CMLS-SAP j -AM(·)=V(x,s j ,c)∈R u×v , we get the attention weighted network:

[0067] Where x is the input image, is the attention function CMLS-SAP of the j-th level sampling module j -AM(·), the j-1th level sampling function is the input image x to the j-1th level sampling module j-1 The ratio is S j-1 ∈(0,1) low-resolution sampling to obtain coordinates c (j-1) And c (j-1) Mapped to the j-1th level sampling module input image x j-1 middle, is the j-th level sampling function, the input image x of the j-th level sampling module j for c (j) is the input image x of the j-th level sampling module j The ratio is S j The low-resolution sampling of ∈(0,1) corresponds to the coordinate mapping of the pathological full-field image, and C is the coordinate mapping of all c (j) The collection composed of.

[0068] The low-resolution sampling ratio of the input image for each level of sampling function is set in advance.

[0069] right Perform Monte Carlo approximation to obtain the function of the j-th level sampling module:

[0070]

[0071] Among them, M refers to the number of sub-image blocks output by each sampling module, c (j-1) ∈C,c (j) ~CMLS-SAP j-1 (V(x,s j-1 ,c (j-1) ))From the distribution V(x,s j-1 ,c (j-1) ) corresponding position extraction, CMLS-SAP j-1 (·) represents the function of the j-1th level sampling module, CMLS-SAP j (x) Use image x j Low-resolution or full-resolution sub-image blocks, from and c (j) The j-level discrete distribution is determined by the j-level low-resolution view V(x,s j ,c (j) ) is obtained. When j = 1, CMLS-SAP j (x) is the selection of typical feature areas on the low-resolution image; subsequently, the typical feature areas are magnified step by step and then sampled until j=N, and then the full-resolution image (pathological full-field image) is performed.

[0072] The loss function of the configurable layered sampling network CMLS is:

[0073]

[0074] in, is the weighted sum of the cross entropy of the sampling modules at all levels, L RZ is the regularization term, β is the weight coefficient of the regularization term, and the default value is 1.0.

[0075] The weighted sum of cross entropy of sampling modules at all levels Contains the cross entropy of the j-th level sampling module and the weight α of the j-th level sampling module (j) Weight α (j) It consists of two parts, including the area ratio ω1 of the output / input image of the j-th level sampling module (j) , and the difference between the ratio of the sum of the gradient values ​​of the image block with the smallest sum of gradient information and the image block with the largest sum of gradient information in all image blocks of the output j-th level and 1

[0076] Among them, M (j) The total number of image blocks output by the j-th level sampling module,

[0077] Among them, tile (j) is the gradient information in the image block output by the j-th level sampling module, and m is the sequence number of the image block output by the j-th level sampling module.

[0078] Weight ω1 (j) Represents the area ratio of the output / input image of the j-th sampling module; the smaller the area ratio, the larger the weight is assigned; the ratio of the number of image blocks output by the j-th sampling module to the sum of the number of image blocks output by all sampling modules can be directly used to achieve fast approximate calculation. Weight ω2 (j) Represents the ratio of the minimum to maximum value of the gradient information in the output image block of the current j-th layer sampling module. The larger the value, the higher the degree of information consistency of the output sampling image block, and the greater the weight given to this part of the output.

[0079] The purpose of introducing a regularization term into the loss function is to keep the model simple while minimizing the training error, while improving the generalization performance of the model and preventing overfitting.

[0080] L RZ =max{||CMLS-SAP1-AM(·)||2,||CMLS-SAP2-AM(·)||2,...,||CMLS-SAP N -AM(·)||2}, where ||CMLS-SAP j−AM(·)||2 is the second-order norm of the network weights of the attention unit of the j-th sampling module.

[0081] The present invention also provides a system for screening typical areas of very large images, comprising an image receiving module, a processing module and a storage module. The image receiving module receives images for training or evaluation and sends the received images to the processing module. The processing module is communicatively connected to the storage module. The storage module is used to store at least one executable instruction. The executable instruction enables the processing module to perform operations corresponding to the above-mentioned method for screening typical areas of very large images based on the images it receives.

[0082] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0083] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A method for screening typical regions of a large image, characterized in that: The following steps are involved: Acquire full-field pathology images; Inputting the pathological full-field image into a configurable hierarchical sampling network, wherein the configurable hierarchical sampling network is used to perform hierarchical sampling on the pathological full-field image to screen out typical areas; The configurable hierarchical sampling network includes cascaded multi-stage sampling modules; The first-level sampling module screens typical regions on the low-resolution image of the pathological full-field image and maps the regions onto the original-resolution pathological full-field image, thereby obtaining a plurality of original-resolution pathological image blocks with the same range as the screened regions, i.e., first-level sub-image blocks; The j-th sampling module performs typical area screening on the low-resolution image of the sub-image block output by the sampling module at the previous level and maps the image onto the full-field pathology image at the original resolution, obtaining multiple pathology image blocks at the original resolution with the same area range as the currently screened area, i.e., the j-th sub-image block, where j is an integer greater than 1; Each level of the sampling module includes a feature extraction unit, an attention weighting unit, and a classification unit; the feature extraction unit extracts features from the pathological full-field image or the sub-image block output by the sampling module of the previous level, and the classification unit classifies the features extracted by the feature extraction unit based on the weights of the attention weighting unit to obtain sub-image blocks; The loss function of the configurable layered sampling network is in, is the weighted sum of the cross entropy of sampling modules at all levels, is the cross entropy of the j-th level sampling module, α (j) is the weight of the j-th level sampling module, ω1 (j) is the area ratio or quantity ratio of the output image blocks of all levels of sampling modules to the output image blocks of the jth level, ω2 (j) It is the difference between the ratio of the sum of the gradient values ​​of the image block with the smallest sum of gradient information and the image block with the largest sum of gradient information among all image blocks output by the j-th level sampling module and 1, Among them, M (j) is the total number of output image blocks of the j-th level sampling module, Among them, tile (j) is the gradient information in the image block output by the j-th level sampling module, m is the sequence number of the image block output by the j-th level sampling module; L RZ =max{||CMLS-SAP1-AM(·)||2,||CMLS-SAP2-AM(·)||2,...,||CMLS-SAP N -AM(·)||2},||CMLS-SAP j -AM(·)||2 is the second-order norm of the network weight of the attention unit of the j-th sampling module, β is the weight coefficient of the regularization term, and N is the total number of sampling modules.

2. The method for screening typical regions of a large image according to claim 1, characterized in that: The function of the j-th level sampling module is: in, is the classification function of the j-th level sampling module, is the feature extraction function of the j-th level sampling module, is the attention function of the j-th level sampling module, c (j) is the input image x of the j-th level sampling module j The ratio is S j The low-resolution sampling of ∈(0,1) corresponds to the coordinate mapping of the pathological full-field image, and C is the coordinate mapping of all c (j) The set of components; j-1th level sampling function is the input image x to the j-1th level sampling module j-1 The ratio is S j-1 ∈(0,1) low-resolution sampling to obtain coordinates c (j-1) And c (j-1) Mapped to the j-1th level sampling module input image x j-1 middle, is the j-th level sampling function, the input image x of the j-th level sampling module j for 3. The method for screening typical regions of a large image according to claim 1, wherein: After Monte Carlo approximation, the function of the j-th level sampling module is: Among them, M refers to the number of sub-image blocks output by each sampling module, c (j-1) ∈C.

4. A system for screening typical regions of large images, characterized in that: The method comprises an image receiving module, a processing module and a storage module. The image receiving module receives images for training or evaluation and sends the received images to the processing module. The processing module is communicatively connected to the storage module. The storage module is used to store at least one executable instruction. The executable instruction enables the processing module to perform an operation corresponding to the method for screening typical areas of large images as described in any one of claims 1 to 3 based on the image it receives.

Images