Adaptation of Weakly Supervised Localization in Histopathology by Debiasing Predictions

• URL: http://arxiv.org/abs/2603.12468v1
• Date: Thu, 12 Mar 2026 21:34:21 GMT
• Title: Adaptation of Weakly Supervised Localization in Histopathology by Debiasing Predictions
• Abstract summary: Weakly Supervised Object localization (WSOL) models enable joint classification and region-of-interest localization in histology images.<n> distributions shift remains a major cause of performance degradation when deployed in a target domain.<n>sfdadep, a method inspired by machine unlearning, formulates SFDA as an iterative process of identifying and correcting prediction bias.
• Score: 31.806257074649196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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