Prediction method and system for sentinel lymph node metastasis of breast cancer and storage medium

Abstract

The invention discloses a breast cancer sentinel lymph node metastasis prediction method and system and a storage medium, and the method comprises the steps: obtaining a WSI with a label as a training data set, and carrying out the preprocessing, and obtaining an image block set; constructing a WSI classification model; pre-training a feature extractor by using the image block set to obtain a feature vector set; inputting the feature vector set into a prototype clustering module, and extracting a plurality of prototypes through clustering; the breast cancer sentinel lymph node WSI is divided into image blocks, and then the image blocks are input into a feature extractor to extract image block features; matching the image block features with a prototype input feature fusion module, generating a soft distribution histogram, and constructing a feature vector of breast cancer sentinel node WSI; and sending the feature vector of the breast cancer sentinel lymph node WSI into a full connection layer to obtain a WSI classification score, and performing transfer judgment. The method can better solve the problem of micro-metastasis identification while maintaining accurate identification of macro-metastasis, so that breast cancer sentinel lymph node metastasis can be accurately diagnosed.

Description

technical field [0001] The invention belongs to the technical field of pathological digital image processing, and in particular relates to a method, system and storage medium for predicting sentinel lymph node metastasis of breast cancer. Background technique [0002] Breast cancer is the predominant cancer in women, and the diagnosis of sentinel lymph node metastases in breast cancer requires a pathologist to look under a microscope on slides of tissue specimens stained with hematoxylin and eosin (H&E), which is tedious, tedious and difficult to diagnose. Error prone process. In recent years, with the rise of computational pathology, the use of digital scanners to scan H&E stained slides and store them as digital pathology whole slide images (WSI), and then use computer vision algorithms, especially deep learning algorithms, to automatically analyze These WSIs help diagnose breast cancer, effectively improving the diagnostic process. Computational pathology has the benefi...

AI Technical Summary

Problems solved by technology

[0005] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and provide a method, system and storage medium for predicting sentinel lymph node metastasis of breast cancer. The problem of metastasis identification to enable accurate diagnosis of breast cancer sentinel lymph node metastasis

Images