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

• URL: http://arxiv.org/abs/2501.03848v1
• Date: Tue, 07 Jan 2025 15:03:55 GMT
• Title: Semise: Semi-supervised learning for severity representation in medical image
• Authors: Dung T. Tran, Hung Vu, Anh Tran, Hieu Pham, Hong Nguyen, Phong Nguyen,
• Abstract summary: SEMISE is a 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.
• Score: 11.145473299479798
• License:
• 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.

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