标题： 基于患者级诊断的溃疡性结肠炎严重程度估计领域自适应 显示英文标题

标题： Domain Adaptation for Ulcerative Colitis Severity Estimation Using Patient-Level Diagnoses

摘要： 估计溃疡性结肠炎（UC）严重程度的方法的发展具有重要意义。 然而，这些方法通常会因医院间成像设备和临床环境的差异而受到领域偏移的影响。 尽管已经提出了一些领域自适应方法来解决领域偏移问题，但它们仍然难以应对目标领域中监督不足或标注成本高的问题。 为了克服这些挑战，我们提出了一种新的弱监督领域自适应方法，该方法利用患者级别的诊断结果作为目标领域的弱监督，这些结果在UC诊断中是常规记录的。 所提出的方法使用共享聚合标记和最大严重度三元组损失，在不同领域之间对类别分布进行对齐，这利用了患者级别诊断是根据每个患者中最严重的区域确定的这一特性。 实验结果表明，我们的方法优于比较性的DA方法，在领域偏移设置中提高了UC严重程度的估计。 显示英文摘要

摘要： 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.