46 citations · 108 across the 33 of their papers we have counts for
38 papers
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…
Class Adaptive Conformal Training
Badr-Eddine Marani, Julio Silva-Rodriguez, Ismail Ben Ayed +3
Deep neural networks have achieved remarkable success across a variety of tasks, yet they often suffer from unreliable probability estimates. As a result, they can be overconfident…
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…
CAPRMIL: Context-Aware Patch Representations for Multiple Instance Learning
Andreas Lolos, Theofilos Christodoulou, Aris L. Moustakas +2
In computational pathology, weak supervision has become the standard for deep learning due to the gigapixel scale of WSIs and the scarcity of pixel-level annotations, with Multiple…
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…
Multimodal Carotid Risk Stratification with Large Vision-Language Models: Benchmarking, Fine-Tuning, and Clinical Insights
Daphne Tsolissou, Theofanis Ganitidis, Konstantinos Mitsis +3
Reliable risk assessment for carotid atheromatous disease remains a major clinical challenge, as it requires integrating diverse clinical and imaging information in a manner that i…
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…
On the Risk of Misleading Reports: Diagnosing Textual Biases in Multimodal Clinical AI
David Restrepo, Ira Ktena, Maria Vakalopoulou +2
Clinical decision-making relies on the integrated analysis of medical images and the associated clinical reports. While Vision-Language Models (VLMs) can offer a unified framework…
Fairness and Robustness of CLIP-Based Models for Chest X-rays
Théo Sourget, David Restrepo, Céline Hudelot +3
Motivated by the strong performance of CLIP-based models in natural image-text domains, recent efforts have adapted these architectures to medical tasks, particularly in radiology,…
SGPMIL: Sparse Gaussian Process Multiple Instance Learning
Andreas Lolos, Stergios Christodoulidis, Aris L. Moustakas +2
Multiple Instance Learning (MIL) offers a natural solution for settings where only coarse, bag-level labels are available, without having access to instance-level annotations. This…
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…
BayesAdapter: enhanced uncertainty estimation in CLIP few-shot adaptation
Pablo Morales-Álvarez, Stergios Christodoulidis, Maria Vakalopoulou +2
The emergence of large pre-trained vision-language models (VLMs) represents a paradigm shift in machine learning, with unprecedented results in a broad span of visual recognition t…
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…
ViG-Bias: Visually Grounded Bias Discovery and Mitigation
Badr-Eddine Marani, Mohamed Hanini, Nihitha Malayarukil +3
The proliferation of machine learning models in critical decision making processes has underscored the need for bias discovery and mitigation strategies. Identifying the reasons be…
Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning
Théo Moutakanni, Piotr Bojanowski, Guillaume Chassagnon +7
AI Foundation models are gaining traction in various applications, including medical fields like radiology. However, medical foundation models are often tested on limited tasks, le…
Certification of Deep Learning Models for Medical Image Segmentation
Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon +3
In medical imaging, segmentation models have known a significant improvement in the past decade and are now used daily in clinical practice. However, similar to classification mode…
On the detection of Out-Of-Distribution samples in Multiple Instance Learning
Loïc Le Bescond, Maria Vakalopoulou, Stergios Christodoulidis +2
The deployment of machine learning solutions in real-world scenarios often involves addressing the challenge of out-of-distribution (OOD) detection. While significant efforts have…
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…
Towards Better Certified Segmentation via Diffusion Models
Othmane Laousy, Alexandre Araujo, Guillaume Chassagnon +4
The robustness of image segmentation has been an important research topic in the past few years as segmentation models have reached production-level accuracy. However, like classif…
Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization
Marin Scalbert, Maria Vakalopoulou, Florent Couzinié-Devy
In Self-Supervised Learning (SSL), models are typically pretrained, fine-tuned, and evaluated on the same domains. However, they tend to perform poorly when evaluated on unseen dom…
Artifact Removal in Histopathology Images
Cameron Dahan, Stergios Christodoulidis, Maria Vakalopoulou +1
In the clinical setting of histopathology, whole-slide image (WSI) artifacts frequently arise, distorting regions of interest, and having a pernicious impact on WSI analysis. Image…
Region-guided CycleGANs for Stain Transfer in Whole Slide Images
Joseph Boyd, Irène Villa, Marie-Christine Mathieu +4
In whole slide imaging, commonly used staining techniques based on hematoxylin and eosin (H&E) and immunohistochemistry (IHC) stains accentuate different aspects of the tissue land…