Adaptation of Weakly Supervised Localization in Histopathology by Debiasing Predictions
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
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2026
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| _version_ | 1866911511633461248 |
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| author | Guichemerre, Alexis Karimian, Banafsheh Belharbi, Soufiane Gillet, Natacha Thome, Nicolas Shamsolmoali, Pourya Shateri, Mohammadhadi McCaffrey, Luke Granger, Eric |
| author_facet | Guichemerre, Alexis Karimian, Banafsheh Belharbi, Soufiane Gillet, Natacha Thome, Nicolas Shamsolmoali, Pourya Shateri, Mohammadhadi McCaffrey, Luke Granger, Eric |
| contents | 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/}} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12468 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptation of Weakly Supervised Localization in Histopathology by Debiasing Predictions Guichemerre, Alexis Karimian, Banafsheh Belharbi, Soufiane Gillet, Natacha Thome, Nicolas Shamsolmoali, Pourya Shateri, Mohammadhadi McCaffrey, Luke Granger, Eric Computer Vision and Pattern Recognition 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/}} |
| title | Adaptation of Weakly Supervised Localization in Histopathology by Debiasing Predictions |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.12468 |