TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning

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

arXiv · 2512.21331

TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning

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

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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.

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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. 2025-12-24. TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning. https://arxiv.org/abs/2512.21331

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