Breast cancer image information bottleneck multi-task classification and segmentation method and system

Abstract

The invention belongs to the technical field of medical image processing, and provides a breast cancer image information bottleneck multi-task classification and segmentation method and system. The method comprises the following steps: acquiring a plurality of breast images of contrast enhanced X-ray photography and corresponding benign and malignant categories and lump position pixel-level labels; performing preprocessing operation on each acquired breast image of the contrast enhanced X-ray photography; adopting a multi-task network to extract multi-task shared representation for each preprocessed mammary gland image; inputting the shared representation to a classification encoder to obtain an encoding tensor of an intermediate layer, and then sending the intermediate encoding tensor to an information bottleneck attribution module for feature compression and benign and malignant classification to obtain a classification task tensor; adapting the shared representation to a segmentation network to obtain a segmentation task tensor; and carrying out feature fusion on the classification task tensor and the segmentation task tensor to segment the focus.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a breast cancer image information bottleneck multi-task classification and segmentation method and system. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Early detection and diagnosis of breast cancer is the key to improving survival and efficacy. Regular physical exams and screenings can help detect breast lumps and suspicious lesions early. Mammography, a commonly used method of breast cancer screening, contributes significantly to reducing breast cancer mortality through early detection of cancer. Breast cancer can be identified based on the position of the radiologist and the analysis of lesions in the images, but this results in a diagnosis that is subject to subjective factors and clinical diagnostic variab...

AI Technical Summary

Problems solved by technology

[0004]Current multi-task collaborative learning methods have achieved remarkable results in many fields, but the interpretability of task associations has been a controversial issue

Despite their widespread application in recent years, deep learning models for multi-tasking are still black-box models without a comprehensive theoretical understanding that can adequately explain them and their internal organization

The inventors found that there is still a lack of effective solutions to the interpretability problems of accurate classification and segmentation of breast cancer images

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