TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Belagali, Varun Kapse, Saarthak Marza, Pierre Das, Srijan Li, Zilinghan Boutaj, Sofiène Pati, Pushpak Yellapragada, Srikar Nandi, Tarak Nath Madduri, Ravi K Saltz, Joel Prasanna, Prateek Christodoulidis, Stergios Vakalopoulou, Maria Samaras, Dimitris |
| author_facet | Belagali, Varun Kapse, Saarthak Marza, Pierre Das, Srijan Li, Zilinghan Boutaj, Sofiène Pati, Pushpak Yellapragada, Srikar Nandi, Tarak Nath Madduri, Ravi K Saltz, Joel Prasanna, Prateek Christodoulidis, Stergios Vakalopoulou, Maria Samaras, Dimitris |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_21331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning Belagali, Varun Kapse, Saarthak Marza, Pierre Das, Srijan Li, Zilinghan Boutaj, Sofiène Pati, Pushpak Yellapragada, Srikar Nandi, Tarak Nath Madduri, Ravi K Saltz, Joel Prasanna, Prateek Christodoulidis, Stergios Vakalopoulou, Maria Samaras, Dimitris Computer Vision and Pattern Recognition 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. |
| title | TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.21331 |