Semise: Semi-supervised learning for severity representation in medical image
This addresses the challenge of limited labeled data for medical image analysis, offering incremental improvements in accuracy for healthcare applications.
The paper tackled the problem of data scarcity in medical imaging by introducing SEMISE, a method combining self-supervised and supervised learning, resulting in a 12% improvement in classification and a 3% improvement in segmentation.
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.