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Colorectal cancer digital pathological image discrimination method and system based on weak supervised learning

A technology for digital pathology and colorectal cancer, applied in the field of pathological images, can solve problems such as difficult data labeling tasks, wrong labeling, and time-consuming

Pending Publication Date: 2021-08-06
ZHEJIANG NORMAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Conventional deep learning algorithms require one-to-one labeling of training data to achieve good performance, and the quality of the data set will greatly affect the algorithm results
Due to the complexity of the digital pathology image format, the task of data labeling at the target magnification becomes difficult, and pathologists spend time and effort on data labeling. At the same time, there may be missing labels and errors in such a large-scale data. Standard and other issues
The way of finely labeling data obviously has certain limitations in the field of medical images
In addition, even with pixel-level labeled data, there is a problem of poor generalization between different data.

Method used

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  • Colorectal cancer digital pathological image discrimination method and system based on weak supervised learning
  • Colorectal cancer digital pathological image discrimination method and system based on weak supervised learning
  • Colorectal cancer digital pathological image discrimination method and system based on weak supervised learning

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0065] The colorectal cancer digital pathology image discrimination system based on weakly supervised learning provided in this embodiment, such as Figure 1-2 shown, including:

[0066] The collection module 11 is used to collect digital pathological image data sets of colorectal cancer;

[0067] The preprocessing module 12 is used to preprocess the data in the collected data set to obtain preprocessed data;

[0068] The first classification module 13 is used to construct a sampling block discrimination model based on a weakly supervised learning algorithm, and input the preprocessed data into the constructed sampling block discrimination model for processing to obtain the classification results of all pathological image blocks in the full-slice sampling package ;

[0069] The second classification module 14 is configured to construct a decision fusion model, and input the obtained classification results of pathological image blocks into the decision fusion model for fusion...

Embodiment 2

[0194] The difference between the colorectal cancer digital pathology image discrimination system based on weakly supervised learning provided in this embodiment and the first embodiment is that:

[0195] In this embodiment, the performance of the algorithm is tested on the lesion-assisted diagnosis of colorectal cancer and the identification of microsatellite instability.

[0196] The experimental configuration is shown in Table 2:

[0197]

[0198] Table 2 Experimental hardware environment configuration

[0199] results and analysis:

[0200] (1) Auxiliary diagnosis of colorectal cancer lesions

[0201] In the lesion-assisted diagnosis task, the colorectal cancer pathology segmentation challenge data from MICCAI is used. The data contains 410 positive samples and 250 negative samples, and all positive samples are marked by pathologists for the lesion area. In this experiment, the purpose is to explore the multi-instance learning algorithm based on coarse-grained annot...

Embodiment 3

[0224] This embodiment provides a colorectal cancer digital pathology image discrimination method based on weakly supervised learning, including:

[0225] S1. Acquisition of colorectal cancer digital pathology image data set;

[0226] S2. Preprocessing the data in the collected data set to obtain preprocessed data;

[0227] S3. Construct a sampling block discrimination model based on a weakly supervised learning algorithm, and input the preprocessed data into the constructed sampling block discrimination model for processing, and obtain the classification results of all pathological image blocks in the full-slice sampling package;

[0228] S4. Build a decision-making fusion model, and input the obtained classification results of the pathological image blocks into the decision-making fusion model for fusion processing to obtain the classification results of the full-digital pathological images.

[0229] Further, the step S2 includes:

[0230] S21. Locate valid tissues in the ...

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Abstract

The invention discloses a colorectal cancer digital pathological image discrimination method and system based on weak supervised learning. The colorectal cancer digital pathological image discrimination system based on weak supervised learning comprises an acquisition module used for collecting a colorectal cancer digital pathological image data set; a preprocessing module used for preprocessing data in the collected data set to obtain preprocessed data; a first classification module used for constructing a sampling block discrimination model based on a weak supervised learning algorithm, inputting the preprocessed data into the constructed sampling block discrimination model for processing, and obtaining a classification result of all pathological image blocks in a full-slice sampling packet; and a second classification module used for constructing a decision fusion model, inputting the obtained classification result of the pathological image blocks into the decision fusion model for fusion processing, and obtaining a classification result of the all-digital pathological image.

Description

technical field [0001] The invention relates to the technical field of pathological images, in particular to a colorectal cancer digital pathological image discrimination method and system based on weakly supervised learning. Background technique [0002] Tumor has always been one of the major diseases that endanger human health. Its morbidity and mortality rate are among the highest in the world, and it is still showing an upward trend year by year in developing countries. Colorectal cancer (CRC) is one of the most common gastrointestinal tumors. The 2018 China Cancer Statistics Report shows that the incidence and mortality of colorectal cancer in my country rank third and fifth among all malignant tumors, respectively. , among which the urban area is much higher than the rural area, and the incidence of colorectal cancer has increased significantly. Studies have shown that its incidence is related to the level of regional economic development, and is greatly affected by li...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G16H30/20G16H50/20
CPCG06N3/084G06N3/08G16H30/20G16H50/20G06N3/045G06F18/25G06F18/24
Inventor 朱信忠徐慧英李军赵建民
Owner ZHEJIANG NORMAL UNIVERSITY
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