{"ID":2825572,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.21331","arxiv_id":"2512.21331","title":"TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning","abstract":"The interpretation of small tiles in large whole slide images (WSI) often needs a larger image context. We introduce TICON, a transformer-based tile representation contextualizer that produces rich, contextualized embeddings for ''any'' application in computational pathology. Standard tile encoder-based pipelines, which extract embeddings of tiles stripped from their context, fail to model the rich slide-level information essential for both local and global tasks. Furthermore, different tile-encoders excel at different downstream tasks. Therefore, a unified model is needed to contextualize embeddings derived from ''any'' tile-level foundation model. TICON addresses this need with a single, shared encoder, pretrained using a masked modeling objective to simultaneously unify and contextualize representations from diverse tile-level pathology foundation models. Our experiments demonstrate that TICON-contextualized embeddings significantly improve performance across many different tasks, establishing new state-of-the-art results on tile-level benchmarks (i.e., HEST-Bench, THUNDER, CATCH) and slide-level benchmarks (i.e., Patho-Bench). Finally, we pretrain an aggregator on TICON to form a slide-level foundation model, using only 11K WSIs, outperforming SoTA slide-level foundation models pretrained with up to 350K WSIs.","short_abstract":"The interpretation of small tiles in large whole slide images (WSI) often needs a larger image context. We introduce TICON, a transformer-based tile representation contextualizer that produces rich, contextualized embeddings for ''any'' application in computational pathology. Standard tile encoder-based pipelines, whic...","url_abs":"https://arxiv.org/abs/2512.21331","url_pdf":"https://arxiv.org/pdf/2512.21331v2","authors":"[\"Varun Belagali\",\"Saarthak Kapse\",\"Pierre Marza\",\"Srijan Das\",\"Zilinghan Li\",\"Sofiène Boutaj\",\"Pushpak Pati\",\"Srikar Yellapragada\",\"Tarak Nath Nandi\",\"Ravi K Madduri\",\"Joel Saltz\",\"Prateek Prasanna\",\"Stergios Christodoulidis\",\"Maria Vakalopoulou\",\"Dimitris Samaras\"]","published":"2025-12-24T18:58:16Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false}