仅用少量样本实现跨域医学图像分割，适合资源有限地区
Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation
- 基于少数示例，通过领域感知选择性适配实现跨域分割
- 在粗略边界框提示下仍保持高精度，优于现有方法近30%
- 无需专家知识，特别适合低收入国家临床应用
医学图像分割面临领域差异、数据模态变化及对领域知识依赖的问题，尤其在低收入和中等收入国家（LMICs）。人类仅需少量标注示例即可分割不同医学图像，而现有基于SAM的模型依赖精细视觉提示（如手工标注掩码生成的边界框），但在实际临床中难以获取此类精确先验。实验表明，先前模型在使用较粗边界框提示时几乎失效。本文提出一种领域感知选择性适配方法，仅需少量（如少于5个）示例，即可将大型自然图像预训练模型的知识有效迁移到医学领域。该方法显著缓解了上述限制，提供高效且适合LMICs的应用方案。大量实验证明其有效性，为医疗诊断与临床应用带来潜在提升。
原文摘要 · Abstract (English)
Medical image segmentation poses challenges due to domain gaps, data modality variations, and dependency on domain knowledge or experts, especially for low- and middle-income countries (LMICs). Whereas for humans, given a few exemplars (with corresponding labels), we are able to segment different medical images even without exten-sive domain-specific clinical training. In addition, current SAM-based medical segmentation models use fine-grained visual prompts, such as the bounding rectangle generated from manually annotated target segmentation mask, as the bounding box (bbox) prompt during the testing phase. However, in actual clinical scenarios, no such precise prior knowledge is available. Our experimental results also reveal that previous models nearly fail to predict when given coarser bbox prompts. Considering these issues, in this paper, we introduce a domain-aware selective adaptation approach to adapt the general knowledge learned from a large model trained with natural images to the corresponding medical domains/modalities, with access to only a few (e.g. less than 5) exemplars. Our method mitigates the aforementioned limitations, providing an efficient and LMICs-friendly solution. Extensive experimental analysis showcases the effectiveness of our approach, offering potential advancements in healthcare diagnostics and clinical applications in LMICs.
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