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cs.CV2026

From Patches to Patients: A study of the tile-to-slide performance transferability in Digital Pathology

Sofiène Boutaj, Leo Fillioux, Maria Vakalopoulou +2

Foundation Models (FMs) have recently redefined the state-of-the-art in histopathology by providing robust representations for whole-slide image (WSI) analysis. However, selecting…

cs.CV2025

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

Varun Belagali, Saarthak Kapse, Pierre Marza +12

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 t…

cs.CV2025

Controllable Latent Space Augmentation for Digital Pathology

Sofiène Boutaj, Marin Scalbert, Pierre Marza +3

Whole slide image (WSI) analysis in digital pathology presents unique challenges due to the gigapixel resolution of WSIs and the scarcity of dense supervision signals. While Multip…

cs.CV20251 cited

THUNDER: Tile-level Histopathology image UNDERstanding benchmark

Pierre Marza, Leo Fillioux, Sofiène Boutaj +6

Progress in a research field can be hard to assess, in particular when many concurrent methods are proposed in a short period of time. This is the case in digital pathology, where…

cs.CV20252 cited

False Promises in Medical Imaging AI? Assessing Validity of Outperformance Claims

Evangelia Christodoulou, Annika Reinke, Pascaline Andrè +23

Performance comparisons are fundamental in medical imaging Artificial Intelligence (AI) research, often driving claims of superiority based on relative improvements in common perfo…

cs.CV2024

Confidence intervals uncovered: Are we ready for real-world medical imaging AI?

Evangelia Christodoulou, Annika Reinke, Rola Houhou +19

Medical imaging is spearheading the AI transformation of healthcare. Performance reporting is key to determine which methods should be translated into clinical practice. Frequently…