用自然语言指导自动分割医学图像，大幅减少专家标注时间。
LINGUAL: Language-INtegrated GUidance in Active Learning for Medical Image Segmentation
- 通过自然语言指令生成可执行程序，自动完成分割任务。
- 在主动域适应中性能优于或相当于传统方法，标注时间减少约80%。
- 适合需要高效标注的医疗影像研究者，尤其擅长处理模糊边界。
尽管主动学习（AL）在分割任务中使专家只需标注感兴趣区域（ROIs），而非整幅图像，但因医学图像中边界模糊且不明确，仍存在高度挑战性、耗时且认知负担重的问题。传统AL中，标注工作量与ROI大小相关：大区域虽易标注但成本高，小区域需更高精度和专注力。在此背景下，语言引导提供了一种低投入的替代方案，避免了精确勾画边界的认知负担。为此，我们提出LINGUAL框架：接收专家的自然语言指令，通过上下文学习将其转化为可执行程序，并自动执行一系列子任务，无需人工干预。我们在主动域适应（ADA）场景中验证了LINGUAL的有效性，其性能与传统AL基线相当或更优，同时将估算标注时间减少了约80%。
原文摘要 · Abstract (English)
Although active learning (AL) in segmentation tasks enables experts to annotate selected regions of interest (ROIs) instead of entire images, it remains highly challenging, labor-intensive, and cognitively demanding due to the blurry and ambiguous boundaries commonly observed in medical images. Also, in conventional AL, annotation effort is a function of the ROI- larger regions make the task cognitively easier but incur higher annotation costs, whereas smaller regions demand finer precision and more attention from the expert. In this context, language guidance provides an effective alternative, requiring minimal expert effort while bypassing the cognitively demanding task of precise boundary delineation in segmentation. Towards this goal, we introduce LINGUAL: a framework that receives natural language instructions from an expert, translates them into executable programs through in-context learning, and automatically performs the corresponding sequence of sub-tasks without any human intervention. We demonstrate the effectiveness of LINGUAL in active domain adaptation (ADA) achieving comparable or superior performance to AL baselines while reducing estimated annotation time by approximately 80%.
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