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Jan, 2025
SEMISE：医学图像严重程度表示的半监督学习
Semise: Semi-supervised learning for severity representation in medical image
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Dung T. Tran, Hung Vu, Anh Tran, Hieu Pham, Hong Nguyen...
TL;DR
本研究提出了一种新方法SEMISE，结合自监督和监督学习来解决医学图像表示学习中的数据稀缺问题。通过利用标记数据和增强数据，该方法显著改进了分类性能（提升12%）和分割性能（提升3%），展示了其在医疗图像分析中的潜力，尤其在标记数据有限的情况下。
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 t
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