Medical image classification method based on collaborative deep learning

Abstract

The invention discloses a medical image classification method based on collaborative deep learning so as to solve the technical problem of poor classification accuracy of an existing medical image classification method. The technical scheme is characterized in that the method adopts a collaborative learning method between two deep convolution neural networks to carry out training in a mode of learning in pairs; each time, a model receives an image pair as input, and one pair of images are transmitted to the corresponding deep convolution neural networks respectively; the deep convolution neural networks are subjected to initialization and training through a pre-training model fine tuning method; a collaborative learning system is designed to allow the two deep networks to realize collaborative learning; and the collaborative learning is used for monitoring different or same attributes of the image pairs, that is, judging whether the image pair belongs to one category, and carrying out back propagation on collaborative errors generated by the two deep convolution neural networks in real time, and collecting network weight, so that the method can further enhance network learning feature representation capability, and can make an accurate judgment for easily-confused samples more effectively.

Description

technical field [0001] The present invention relates to a medical image classification method, in particular to a medical image classification method based on collaborative deep learning. Background technique [0002] Medical image classification methods play an extremely important role in medical retrieval, literature review and medical research, and have always been a hot research issue in the field of computer-aided diagnosis and medical research. In the past decades of research by countless researchers, a complete set of image classification techniques under the traditional model has been formed. Its core elements are two parts: manual feature extraction and classifier design. Although there is a very complete theoretical system, it is difficult for traditional image classification methods to achieve the seamless combination of optimal features and optimal classifiers, which leads to a great impact on their performance. In recent years, the emergence of deep learning t...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of poor classification accuracy of existing medical image classificatio

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