most citedTHUNDER: Tile-level Histopathology image UNDERstanding benchmark

1 citations · 1 across the 7 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Scaling Multi-Teacher Distillation for Digital Pathology

Sofiène Boutaj, Pierre Marza, Varun Belagali +3

Multi-teacher distillation allows transferring knowledge from multiple "teacher" networks to a single "student" network. This method is promising in fields such as digital patholog…

cs.CV2026

CAR-MIL: Counterfactual Attention Regularization for Multiple Instance Learning

Imane Chraki, Pierre Marza, Stergios Christodoulidis +1

Multiple Instance Learning (MIL) is widely used for weakly supervised learning, particularly in digital pathology, where fine-grained annotations are costly. Most MIL methods aggre…

cs.CV2026

Pathologist Attention-Aligned Report Generation for Prostate Histopathology

Ruoyu Xue, Suryakant Singh, Souradeep Chakraborty +12

The allocation of visual attention by pathologists during cancer diagnosis is a highly selective process that critically shapes the information extracted from whole-slide images (W…

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.CV2026

Information Maximization for Long-Tailed Semi-Supervised Domain Generalization

Leo Fillioux, Omprakash Chakraborty, Quentin Gopée +6

Semi-supervised domain generalization (SSDG) has recently emerged as an appealing alternative to tackle domain generalization when labeled data is scarce but unlabeled samples acro…

cs.CV2026

OCTOPUS: Enhancing the Spatial-Awareness of Vision SSMs with Multi-Dimensional Scans and Traversal Selection

Kunal Mahatha, Ali Bahri, Pierre Marza +5

State space models (SSMs) have recently emerged as an alternative to transformers due to their unique ability of modeling global relationships in text with linear complexity. Howev…

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.CV2025★ 1 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.CV2025

CDG-MAE: Cross-view Masked Modeling using Diffusion Generated Views

Varun Belagali, Pierre Marza, Srikar Yellapragada +7

Cross-view masked autoencoding has emerged as a powerful pretext task for learning dense correspondences, which are essential for applications such as video label propagation. The…