LPD: Learnable Prototypes with Diversity Regularization for Weakly Supervised Histopathology Segmentation

• URL: http://arxiv.org/abs/2512.05922v1
• Date: Fri, 05 Dec 2025 17:59:16 GMT
• Title: LPD: Learnable Prototypes with Diversity Regularization for Weakly Supervised Histopathology Segmentation
• Authors: Khang Le, Anh Mai Vu, Thi Kim Trang Vo, Ha Thach, Ngoc Bui Lam Quang, Thanh-Huy Nguyen, Minh H. N. Le, Zhu Han, Chandra Mohan, Hien Van Nguyen,
• Abstract summary: Weakly supervised semantic segmentation (WSSS) in histopathology is hindered by inter-class homogeneity, intra-class heterogeneity, and CAM-induced region shrinkage.<n>We propose a cluster-free, one-stage learnable-prototype framework with diversity regularization to enhance morphological intra-class heterogeneity coverage.<n>Our approach achieves state-of-the-art (SOTA) performance on BCSS-WSSS, outperforming prior methods in mIoU and mDice.
• Score: 17.25487101903999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised semantic segmentation (WSSS) in histopathology reduces pixel-level labeling by learning from image-level labels, but it is hindered by inter-class homogeneity, intra-class heterogeneity, and CAM-induced region shrinkage (global pooling-based class activation maps whose activations highlight only the most distinctive areas and miss nearby class regions). Recent works address these challenges by constructing a clustering prototype bank and then refining masks in a separate stage; however, such two-stage pipelines are costly, sensitive to hyperparameters, and decouple prototype discovery from segmentation learning, limiting their effectiveness and efficiency. We propose a cluster-free, one-stage learnable-prototype framework with diversity regularization to enhance morphological intra-class heterogeneity coverage. Our approach achieves state-of-the-art (SOTA) performance on BCSS-WSSS, outperforming prior methods in mIoU and mDice. Qualitative segmentation maps show sharper boundaries and fewer mislabels, and activation heatmaps further reveal that, compared with clustering-based prototypes, our learnable prototypes cover more diverse and complementary regions within each class, providing consistent qualitative evidence for their effectiveness.

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