Abstract
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.
Abstract (translated)
开发用于估计溃疡性结肠炎（UC）严重程度的方法具有重要意义。然而，这些方法常常因为不同医院在成像设备和临床设置上的差异而遭受领域偏移的问题。尽管已经提出了几种域适应方法来解决领域偏移问题，但它们仍然面临目标领域缺乏监督或标注成本高昂的挑战。为了解决这些问题，我们提出了一种新颖的弱监督域自适应方法，该方法利用了在UC诊断中常规记录的患者级别的诊断结果作为目标领域的弱监督信号。我们的方法通过共享聚合令牌（Shared Aggregation Tokens）和最大严重度三元组损失（Max-Severity Triplet Loss）跨领域对类别分布进行对齐，这一策略利用了这样一个特性：患者的最终诊断是由每个患者体内最严重的区域决定的。实验结果表明，我们的方法在域偏移设置下优于比较性的域适应方法，从而提高了UC严重程度估计的准确性。
URL
https://arxiv.org/abs/2509.14573
