Single-Stage Hierarchical Rectification for Weakly Supervised Histopathology Segmentation

• URL: http://arxiv.org/abs/2606.20250v1
• Date: Thu, 18 Jun 2026 14:01:58 GMT
• Title: Single-Stage Hierarchical Rectification for Weakly Supervised Histopathology Segmentation
• Abstract summary: Existing weakly supervised semantic segmentation methods rely on a multi-stage paradigm.<n>We propose the Single-Stage Hierarchical Rectification (SSHR) framework.<n>SSHR proactively purifies intermediate feature representations during the forward pass.
• Score: 0.9799637101641149
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing weakly supervised semantic segmentation (WSSS) methods in computational pathology rely on a multi-stage paradigm: class activation map (CAM) generation, offline pseudo-mask refinement, and fully supervised retraining. While established, this decoupled approach presents fundamental limitations. The multi-stage process not only incurs high computational training costs but also suffers from error propagation: local texture biases in shallow CNN layers generate false-positive artifacts that subsequent refinement steps often fail to correct. To address these persistent challenges through a simple yet highly effective approach, we propose the Single-Stage Hierarchical Rectification (SSHR) framework. Rather than passively refining CAMs post-hoc, our method proactively purifies intermediate feature representations during the forward pass. We introduce a Hierarchical Feature Rectification Module (HFRM) that utilizes deep global semantic context to filter out local anomalies in shallow layers. This mechanism generates high-fidelity activation maps directly within a single training loop. Experiments on the LUAD-HistoSeg and BCSS datasets demonstrate that SSHR outperforms state-of-the-art multi-stage methods. Furthermore, SSHR reduces training duration by 2 to 5 times. This efficiency minimizes computational overhead and accelerates clinical translation for large-scale histopathology workflows. The code is available at: https://github.com/trongduc-nguyen/SSHR

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