2 citations · 4 across the 10 of their papers we have counts for
11 papers
CalCErt: Bin-wise Certification of Confidence Calibration in Medical Image Classification
Leo Fillioux, Stergios Christodoulidis, Maria Vakalopoulou +1
Deep neural networks remain vulnerable to adversarial perturbations, which can distort not only predictions but also confidence scores, undermining uncertainty calibration. While e…
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…
Full Conformal Adaptation of Medical Vision-Language Models
Julio Silva-Rodríguez, Leo Fillioux, Paul-Henry Cournède +4
Vision-language models (VLMs) pre-trained at large scale have shown unprecedented transferability capabilities and are being progressively integrated into medical image analysis. A…
Are foundation models for computer vision good conformal predictors?
Leo Fillioux, Julio Silva-Rodríguez, Ismail Ben Ayed +4
Recent advances in self-supervision and contrastive learning have brought the performance of foundation models to unprecedented levels in a variety of tasks. Fueled by this progres…
Spatio-Temporal Analysis of Patient-Derived Organoid Videos Using Deep Learning for the Prediction of Drug Efficacy
Leo Fillioux, Emilie Gontran, Jérôme Cartry +7
Over the last ten years, Patient-Derived Organoids (PDOs) emerged as the most reliable technology to generate ex-vivo tumor avatars. PDOs retain the main characteristics of their o…
Structured State Space Models for Multiple Instance Learning in Digital Pathology
Leo Fillioux, Joseph Boyd, Maria Vakalopoulou +2
Multiple instance learning is an ideal mode of analysis for histopathology data, where vast whole slide images are typically annotated with a single global label. In such cases, a…