Automatic diagnosis of cardiac magnetic resonance images based on semi-supervised learning

TL;DR

This paper presents a semi-supervised learning model for automatic cardiac MRI segmentation and diagnosis, reducing the need for annotated data while maintaining high accuracy to assist physicians.

Contribution

It introduces a novel semi-supervised approach that achieves high-precision cardiac image segmentation and disease prediction with limited annotated data.

Findings

High accuracy in cardiac MRI segmentation

Effective disease prediction from MRI features

Reduced need for extensive annotated datasets

Abstract

Cardiac magnetic resonance imaging (MRI) is a pivotal tool for assessing cardiac function. Precise segmentation of cardiac structures is imperative for accurate cardiac functional evaluation. This paper introduces a semi-supervised model for automatic segmentation of cardiac images and auxiliary diagnosis. By harnessing cardiac MRI images and necessitating only a small portion of annotated image data, the model achieves fully automated, high-precision segmentation of cardiac images, extraction of features, calculation of clinical indices, and prediction of diseases. The provided segmentation results, clinical indices, and prediction outcomes can aid physicians in diagnosis, thereby serving as auxiliary diagnostic tools. Experimental results showcase that this semi-supervised model for automatic segmentation of cardiac images and auxiliary diagnosis attains high accuracy in segmentation and correctness in prediction, demonstrating substantial practical guidance and application value.