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.
Abstract (translated)
本文介绍了SEMISE,这是一种新颖的医疗影像表示学习方法,结合了自监督和监督学习。通过利用标记数据和增强数据,SEMISE解决了数据稀缺的问题,并增强了编码器提取有意义特征的能力。这种集成的方法产生了更具信息量的表示形式,从而提高了下游任务的表现。结果表明,我们的方法在分类任务上取得了12%的改进,在分割任务上取得了3%的改进,优于现有的方法。这些结果显示了SIMESE(原文中似乎是拼写错误,应该是SEMISE)在推进医疗影像分析方面的潜力,并为医疗应用提供更准确的解决方案,尤其是在标记数据有限的情况下。
URL
https://arxiv.org/abs/2501.03848