Semise: Semi-supervised learning for severity representation in medical image

TL;DR

SEMISE is a semi-supervised learning method for medical image representation that combines self-supervised and supervised techniques, improving downstream task performance especially with limited labeled data.

Contribution

It introduces SEMISE, a novel semi-supervised approach that enhances feature extraction in medical imaging by leveraging both labeled and unlabeled data.

Findings

12% improvement in classification accuracy

3% improvement in segmentation performance

Outperforms existing methods in medical image analysis

Abstract

This paper introduces SEMISE, a novel method for representation learning in medical imaging that combines self-supervised and supervised learning. By leveraging both labeled and augmented data, SEMISE addresses the challenge of data scarcity and enhances the encoder's ability to extract meaningful features. This integrated approach leads to more informative representations, improving performance on downstream tasks. As result, our approach achieved a 12% improvement in classification and a 3% improvement in segmentation, outperforming existing methods. These results demonstrate the potential of SIMESE to advance medical image analysis and offer more accurate solutions for healthcare applications, particularly in contexts where labeled data is limited.