Chest radiography multi-label classification method based on self-correcting label generation network

Abstract

The invention belongs to the field of medical image processing, and particularly relates to a chest radiography multi-label classification method based on a self-correcting label generation network. According to the invention, a self-correcting label generation network model is constructed and is used for multi-label classification of chest radiography; the network comprises a ResNet image encoder, a self-correcting attention mechanism module and a GRU decoder, the image encoder is used for acquiring high-level semantic features of an input image, namely a chest radiography image, generating image feature representation and outputting the image feature representation to the self-correcting attention mechanism module; the self-correcting attention mechanism module generates an attention mapcorresponding to the current time step at each moment according to the state information and the image features output at the previous moment, and outputs a context feature vector to the decoder; andthe decoder generates a label corresponding to the image at the current moment according to the context feature vector and a label word vector generated at the previous moment. According to the method, the problem of unbalanced prediction effect in chest radiography multi-label classification is effectively solved.

Description

technical field [0001] The invention belongs to the field of medical image processing, and in particular relates to a multi-label classification method for chest radiographs. Background technique [0002] Chest radiography is widely used in clinical diagnosis and treatment, and is one of the main means of detecting and diagnosing chest abnormalities. Specialized physicians read the chest radiographs and wrote text reports explaining the findings. This is a job that requires rich medical experience and high concentration. For inexperienced doctors, it is easy to make mistakes in diagnosing medical images. And for doctors in densely populated areas, reading medical images is time-consuming and tedious. Therefore, it is very important to study computer-aided diagnosis and treatment algorithms to help doctors better diagnose medical images. [0003] In general, chest radiographs usually contain one or more disease labels, so this is an image multi-label classification problem...

AI Technical Summary

Problems solved by technology

[0007] The object of the present invention is to provide a chest radiograph multi-label classification method that can solve the unbalanced prediction effect phenomenon generated on the chest radiograph multi-label classification problem

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