{"ID":2832656,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.05922","arxiv_id":"2512.05922","title":"LPD: Learnable Prototypes with Diversity Regularization for Weakly Supervised Histopathology Segmentation","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.","short_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 m...","url_abs":"https://arxiv.org/abs/2512.05922","url_pdf":"https://arxiv.org/pdf/2512.05922v1","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\"]","published":"2025-12-05T17:59:16Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}