用反事实生成提升医学图像分割的自监督学习效果
Pixel-level Counterfactual Contrastive Learning for Medical Image Segmentation
- 通过双视角与多视角对比学习，结合反事实图像增强特征表示
- 无需标注数据时达94%分割准确率，使用银标准标签更优
- 适合医学图像分割领域，尤其数据标注难的场景
医学图像分割依赖大量标注数据，获取成本高。银标准（AI生成）标签虽易得，但可能引入偏差。自监督学习仅需图像，成为预训练关键方法。近期工作将对比学习与反事实生成结合，提升了分类任务表征能力，但难以扩展至像素级任务。本文提出结合反事实生成与密集对比学习的双视角（DVD-CL）和多视角（MVD-CL）方法，以及利用银标准标注的监督变体。还引入新的可视化算法——彩色高分辨率叠加图（CHRO-map）。实验表明，无标注的DVD-CL优于其他密集对比学习方法；使用银标准标签的监督变体优于直接使用银标准标注训练，在挑战性数据上达到约94% DSC。结果表明，结合反事实与银标准标注的像素级对比学习，能有效提升对采集和病理变化的鲁棒性。
原文摘要 · Abstract (English)
Image segmentation relies on large annotated datasets, which are expensive and slow to produce. Silver-standard (AI-generated) labels are easier to obtain, but they risk introducing bias. Self-supervised learning, needing only images, has become key for pre-training. Recent work combining contrastive learning with counterfactual generation improves representation learning for classification but does not readily extend to pixel-level tasks. We propose a pipeline combining counterfactual generation with dense contrastive learning via Dual-View (DVD-CL) and Multi-View (MVD-CL) methods, along with supervised variants that utilize available silver-standard annotations. A new visualisation algorithm, the Color-coded High Resolution Overlay map (CHRO-map) is also introduced. Experiments show annotation-free DVD-CL outperforms other dense contrastive learning methods, while supervised variants using silver-standard labels outperform training on the silver-standard labeled data directly, achieving $\sim$94% DSC on challenging data. These results highlight that pixel-level contrastive learning, enhanced by counterfactuals and silver-standard annotations, improves robustness to acquisition and pathological variations.
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