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• Distribution shifts in target domains cause performance degradation in Weakly Supervised Object Localization (WSOL) models for histology images.limitation 70%
• Under stronger cross-domain shifts, WSOL predictions can become biased toward dominant classes, leading to skewed pseudo-label distributions.limitation 70%
• Source-Free (Unsupervised) Domain Adaptation (SFDA) methods, when applied to WSOL, can reinforce initial prediction bias through self-training, degrading performance.limitation 70%
• SFDA-DeP is a novel method for SFDA of WSOL models in histopathology that addresses prediction bias by identifying and correcting it iteratively.method 70%
• SFDA-DeP periodically identifies target images from over-predicted classes and selectively reduces predictive confidence for uncertain (high entropy) images.method 70%
• A jointly optimized pixel-level classifier is used in SFDA-DeP to restore discriminative localization features under distribution shift.method 70%
• SFDA-DeP consistently improves classification and localization performance over state-of-the-art SFDA baselines on cross-organ and -center histopathology benchmarks.result 70%

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