Medical image segmentation or classification method based on small sample domain self-adaption

Abstract

The invention discloses a medical image segmentation or classification method based on small sample domain self-adaption, and the method comprises the steps: downloading medical image data and clinical image data from a public data set, and taking the processed medical image data as a source domain training data set with a known label; using the processed clinical image data as a to-be-classified or segmented target domain data set, marking a very small amount of clinical image data by a doctor, constructing a small sample domain self-adaption model by a feature extractor and a classifier realized by a convolutional neural network, and training to obtain a trained small sample domain self-adaption model; and inputting a to-be-classified or segmented target domain data set into the small sample domainself-adaption model with the best classification effect to obtain a classification or segmentation result to which the focus of the target domain data set belongs. Different objective functions are adopted according to whether the data contain labels or not, cross-domain migration of the small sample domain self-adaption model is achieved, and the method is applied to classification or segmentation of image data of clinic, pathology, ultrasound and the like.

Description

technical field [0001] The invention belongs to the technical field of medical image classification, in particular to a medical image segmentation or classification method based on small sample domain self-adaptation. Background technique [0002] In recent years, with the continuous development and progress of medical imaging technology and computer technology, medical image analysis has become an indispensable tool and technical means in medical research, clinical disease diagnosis and treatment. Traditional medical image analysis is initially mainly through detailed screening and screening by doctors to make corresponding diagnoses, including methods such as edge detection, texture features, morphological filtering, and template matching. However, the daily medical imaging data in the hospital is huge, and the repetitive work may cause doctors to make mistakes in judgment due to fatigue. [0003] Thanks to the ever-increasing computing power and growing amount of availab...

AI Technical Summary

Problems solved by technology

[0004] However, there are still two major challenges in the classification or segmentation of clinical medical images. On the one hand, there are very few labeled data in clinical datasets, and the cost of doctors' labeling is high, time-consuming and laborious.

On the other hand, most of the deep learning models depend on specific data sets, and when the model trained on a specific data set is migrated to other data sets, the diagnostic results will be greatly biased

For different medical imaging equipment, due to different imaging principles, parameters and other factors, they have different degrees of characterization of lesions, so the diagnostic accuracy cannot be guaranteed

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