用迭代可解释性把粗略标注转成精准病灶分割，提升肺栓塞检测效果。
Iterative Explainability for Weakly Supervised Segmentation in Medical PE Detection
- 通过迭代生成软分割图，逐步挖掘被忽略的血栓区域
- 模型在每轮迭代后性能提升，最终达到强监督水平
- 适合缺乏精细标注的医学图像分割任务
肺栓塞（PE）是心血管死亡的主要原因之一。计算机断层扫描肺动脉造影（CTPA）是诊断金标准，人工智能辅助诊断日益受到关注。然而，现有算法受限于血栓负荷的细粒度标注稀缺。本文提出iExplain，一种弱监督学习方法，通过迭代模型可解释性将粗略的图像级标注转化为像素级的肺栓塞掩膜。该方法生成软分割图用于遮蔽检测区域，使过程可重复，从而发现单次扫描遗漏的血栓。这种迭代优化有效捕捉完整病变区域并识别多个独立血栓。基于自动生成标注训练的模型在性能上持续提升，每轮迭代均有显著改进。我们在增强版RSPECT数据集上验证了iExplain的有效性，结果与强监督方法相当，并优于现有弱监督方法。
原文摘要 · Abstract (English)
Pulmonary Embolism (PE) are a leading cause of cardiovascular death. Computed tomographic pulmonary angiography (CTPA) is the gold standard for PE diagnosis, with growing interest in AI-based diagnostic assistance. However, these algorithms are limited by scarce fine-grained annotations of thromboembolic burden. We address this challenge with iExplain, a weakly supervised learning algorithm that transforms coarse image-level annotations into detailed pixel-level PE masks through iterative model explainability. Our approach generates soft segmentation maps used to mask detected regions, enabling the process to repeat and discover additional embolisms that would be missed in a single pass. This iterative refinement effectively captures complete PE regions and detects multiple distinct embolisms. Models trained on these automatically generated annotations achieve excellent PE detection performance, with significant improvements at each iteration. We demonstrate iExplain's effectiveness on the RSPECT augmented dataset, achieving results comparable to strongly supervised methods while outperforming existing weakly supervised methods.
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