Method for realizing medical image multi-task auxiliary diagnosis based on deep learning

Abstract

The invention provides a method for realizing medical image multi-task auxiliary diagnosis based on deep learning. On the condition that a focus position is known, multi-task auxiliary diagnosis of afocus is realized, such as focus area classification, segmentation, meridian regression, etc. The method comprises main steps of extracting medical image focus area data, constructing a multi-label dataset which corresponds with multi-task auxiliary diagnosis; building a single-model multi-task deep learning network, and realizing multi-task model preliminary training; adjusting a learning strategy, selecting a certain single task for training until convergence; fixing the trained single task and a trunk coding network parameter, and performing one-by-one training of other branch tasks. The method can settle a problem of single function of a medical image algorithm and simultaneously realizes multi-task auxiliary diagnosis of the focus by means of single-model single-input. Furthermore, the coding network in the characteristic extracting process is shared in training different tasks, thereby realizing mutual supplementing, and realizing performance which is not lower than or even exceed a single task in different branch tasks.

Description

technical field [0001] The invention relates to the technical field of computer-aided diagnosis of medical images, in particular to a method for realizing multi-task auxiliary diagnosis of medical images based on deep learning. Background technique [0002] Most of the algorithm modules in the current intelligent diagnosis system are single-task models. Although they can provide auxiliary information for doctors to a certain extent, for complex medical conditions, the small amount of information provided by single-task modules for doctors is obviously not enough. Developing multiple modules to cooperate to provide doctors with various auxiliary information can give doctors more useful information to a certain extent, but the development of different modules is often relatively independent, which brings many problems. question. The first is that the development cost is too high. Multiple modules are developed independently, and the cost is several times that of a single modu...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a method for realizing multi-task auxiliary diagnosis of medical images based on deep learning, aiming to solve the

Images