利用患者级诊断信息，提升溃疡性结肠炎严重程度评估在跨医院场景下的准确率
Domain Adaptation for Ulcerative Colitis Severity Estimation Using Patient-Level Diagnoses
- 用患者整体诊断结果作为弱监督信号，缓解目标域标注稀缺问题
- 通过共享聚合标记和最大严重度三元组损失对齐跨域类别分布
- 特别适合医疗影像跨中心迁移任务，减少标注成本
溃疡性结肠炎（UC）严重程度评估方法的开发具有重要意义。然而，这些方法常因不同医院间成像设备与临床设置差异导致领域偏移。尽管已有多种领域自适应方法，仍面临目标域缺乏标注或标注成本高的问题。为此，我们提出一种新型弱监督领域自适应方法，利用常规记录的患者级诊断结果作为目标域的弱监督信号。该方法通过共享聚合标记和最大严重度三元组损失对齐跨域类别分布，充分利用患者诊断由最严重病灶决定的特性。实验表明，所提方法在领域偏移环境下优于现有对比方法，显著提升了溃疡性结肠炎严重程度估计性能。
原文摘要 · Abstract (English)
The development of methods to estimate the severity of Ulcerative Colitis (UC) is of significant importance. However, these methods often suffer from domain shifts caused by differences in imaging devices and clinical settings across hospitals. Although several domain adaptation methods have been proposed to address domain shift, they still struggle with the lack of supervision in the target domain or the high cost of annotation. To overcome these challenges, we propose a novel Weakly Supervised Domain Adaptation method that leverages patient-level diagnostic results, which are routinely recorded in UC diagnosis, as weak supervision in the target domain. The proposed method aligns class-wise distributions across domains using Shared Aggregation Tokens and a Max-Severity Triplet Loss, which leverages the characteristic that patient-level diagnoses are determined by the most severe region within each patient. Experimental results demonstrate that our method outperforms comparative DA approaches, improving UC severity estimation in a domain-shifted setting.
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