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This paper introduces ProBAG, a novel pseudo-mask generator for weakly supervised semantic segmentation in histopathology that leverages both visual and text prototypes to enhance segmentation accuracy. By employing class-wise power recalibration and one-step graph diffusion, ProBAG effectively addresses the limitations of CAM-based methods, particularly in localizing tissue interfaces. Experimental results demonstrate significant performance improvements over existing weakly supervised segmentation techniques, highlighting the importance of integrating pathology-aligned text semantics and graph refinement.
ProBAG achieves superior segmentation accuracy by combining visual and text prototypes, significantly improving weakly supervised histopathology segmentation without relying on external models.
Weakly supervised semantic segmentation enables histopathology tissue segmentation from image-level annotations, avoiding costly pixel-level labeling by expert pathologists. However, CAM-based methods often localize only highly discriminative regions and remain unreliable near tissue interfaces. We propose ProBAG, a stage-1 pseudo-mask generator that combines dataset-specific visual prototypes with pathology-aligned CONCH text prototypes over multi-scale frozen UNI features. ProBAG introduces two complementary mechanisms: class-wise power recalibration that reshapes inter-class competition while preserving the total foreground activation mass at each pixel, and one-step graph diffusion in which feature affinities are penalized by a late-transformer attention-context discrepancy used as a soft structural boundary cue. The resulting stage-1 pseudo-masks require neither CRF nor an external segmentation model; for complete two-stage comparison, they additionally supervise a downstream Phikon-FPN segmenter. Experiments on BCSS-WSSS and LUAD-HistoSeg show consistent gains over recent WSSS approaches, while ablations indicate that pathology-aligned text semantics provide the largest improvement and graph refinement provides a smaller complementary gain. The code is available at: https://github.com/wterrr/WSSS
