Automatic diagnosis of cardiac magnetic resonance images based on semi-supervised learning
This provides an auxiliary diagnostic tool for physicians to improve cardiac function assessment, but it is incremental as it builds on existing semi-supervised methods for medical imaging.
The paper tackles the problem of automating cardiac MRI segmentation and diagnosis using a semi-supervised model that requires only a small portion of annotated data, achieving high accuracy in segmentation and prediction.
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.