2 citations · 4 across the 10 of their papers we have counts for
10 papers · 1 filter
Quantile Adaptive Temperature Scaling for Confidence Calibration
Omprakash Chakraborty, Leo Fillioux, Ismail Ben Ayed +1
Deep neural networks often produce poorly calibrated confidence estimates, overstating their certainty even when predictions are incorrect. Temperature Scaling remains the most wid…
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…
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…
SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning
Leo Fillioux, Omprakash Chakraborty, Ismail Ben Ayed +4
With the increasing adoption of vision-language models (VLMs) in critical decision-making systems such as healthcare or autonomous driving, the calibration of their uncertainty est…
PVeRA: Probabilistic Vector-Based Random Matrix Adaptation
Leo Fillioux, Enzo Ferrante, Paul-Henry Cournède +2
Large foundation models have emerged in the last years and are pushing performance boundaries for a variety of tasks. Training or even finetuning such models demands vast datasets…
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…