Pathological image analysis method based on deformation representation learning

Abstract

The invention belongs to the technical field of medical image processing, and relates to a pathological image analysis method based on deformation representation learning. The method comprises the following step that: a self-supervised deformation representation learning model is constructed, wherein the self-supervised deformation representation learning model is used for pathological image analysis and then used for classification and segmentation of a pathological image, wherein the learning model comprises a deformation module, a local heterogeneous feature sensing module and a global homogeneous feature sensing module, wherein the deformation module is used for performing elastic deformation operation on the image, the local heterogeneous feature sensing module is used for learning structural difference information caused by deformation of a local area in the image, and the module comprises a feature extractor network, a multi-scale feature network and a discriminator network. and the global homogeneous feature sensing module is used for realizing the learning process of the network on the global features of the pathological image. According to the method, the capability of extracting local structural features can be learned without marking data, and the global semantic information of the pathological image can be learned; compared with the best self-supervised learning method at present, the method of the invention is greatly improved in performance.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a pathological image analysis method. Background technique [0002] In recent years, deep learning has achieved exciting results in many different computer vision applications. As one of the problems worth exploring, automated pathological image analysis is in great demand and market, because it can provide reliable statistical data for subsequent analysis and clinical diagnosis, especially in some important diseases (such as cancer, survival prediction, etc.) ) plays an important role in the diagnosis and intervention treatment. Many successful methods now use fully supervised learning to extract effective features in images, but the training process of these models requires a large number of labels as annotation information to construct supervisory signals, which is very difficult in practical applications. The main reason is that a large amount o...

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For example, for benign images, the gland structure is very regular, usually appearing as round, oval, etc.; but for poorly diff

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