用自监督+监督学习提升医疗图像严重程度表征效果
Semise: Semi-supervised learning for severity representation in medical image
- 结合标注数据与增强数据，融合自监督与监督学习
- 分类任务提升12%，分割任务提升3%优于现有方法
- 适合标签数据稀缺的医疗图像分析场景
本文提出 SEMISE，一种融合自监督与监督学习的医学图像表示学习新方法。通过利用少量标注数据和数据增强生成的伪标签，该方法缓解数据稀缺问题，增强编码器提取有意义特征的能力。实验表明，该方法在下游任务中实现分类性能提升12%、分割性能提升3%，显著优于现有方法。结果证明 SEMISE 在医疗图像分析中的潜力，尤其适用于标注数据有限的临床场景。
原文摘要 · Abstract (English)
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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