解决病理图像弱监督定位中的域偏移问题，提升跨机构模型泛化能力。
Adaptation of Weakly Supervised Localization in Histopathology by Debiasing Predictions
- 通过识别并修正目标域中过度预测类别的偏差，迭代优化预测结果。
- 在多个跨器官、跨中心数据集上，分类与定位性能均优于现有自监督域适应方法。
- 特别适合医疗影像领域中因染色、扫描差异导致的模型部署难题。
弱监督目标定位（WSOL）模型仅需图像级标签即可实现组织病理图像的分类与病灶定位。但在新器官或不同机构间部署时，由于染色协议和扫描设备差异导致的数据分布偏移，模型性能显著下降。尤其在强域偏移下，WSOL预测易偏向主导类别，产生高度倾斜的伪标签分布。现有无源域适应（SFDA）方法依赖自训练，会不断强化初始偏差，损害分类与定位任务。本文提出SFDA-DeP，受机器遗忘启发，将域适应建模为迭代识别并修正预测偏差的过程：周期性识别目标域中被过预测的类别，对高熵不确定样本降低预测置信度，同时保留高置信预测，从而缓解决策边界漂移与类别偏倚。联合优化的像素级分类器进一步恢复分布偏移下的判别性定位特征。在GLAS、CAMELYON-16、CAMELYON-17等跨器官与跨中心病理数据集上，采用多种WSOL模型进行实验，结果表明，SFDA-DeP始终优于当前最优的SFDA基线，在分类与定位任务上均有稳定提升。
原文摘要 · Abstract (English)
Weakly Supervised Object Localization (WSOL) models enable joint classification and region-of-interest localization in histology images using only image-class supervision. When deployed in a target domain, distributions shift remains a major cause of performance degradation, especially when applied on new organs or institutions with different staining protocols and scanner characteristics. Under stronger cross-domain shifts, WSOL predictions can become biased toward dominant classes, producing highly skewed pseudo-label distributions in the target domain. Source-Free (Unsupervised) Domain Adaptation (SFDA) methods are commonly employed to address domain shift. However, because they rely on self-training, the initial bias is reinforced over training iterations, degrading both classification and localization tasks. We identify this amplification of prediction bias as a primary obstacle to the SFDA of WSOL models in histopathology. This paper introduces \sfdadep, a method inspired by machine unlearning that formulates SFDA as an iterative process of identifying and correcting prediction bias. It periodically identifies target images from over-predicted classes and selectively reduces the predictive confidence for uncertain (high entropy) images, while preserving confident predictions. This process reduces the drift of decision boundaries and bias toward dominant classes. A jointly optimized pixel-level classifier further restores discriminative localization features under distribution shift. Extensive experiments on cross-organ and -center histopathology benchmarks (glas, CAMELYON-16, CAMELYON-17) with several WSOL models show that SFDA-DeP consistently improves classification and localization over state-of-the-art SFDA baselines. {\small Code: \href{https://anonymous.4open.science/r/SFDA-DeP-1797/}{anonymous.4open.science/r/SFDA-DeP-1797/}}
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