Publications by Guillaume Marc Georges Vray

Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

In this paper, we address the challenge of few-shot classification in histopathology whole slide images (WSIs) by utilizing foundational vision-language models (VLMs) and slide-level prompt learning. Given the gigapixel scale of WSIs, conventional multiple ...

IEEE2025

Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories. We propose a practica ...

Institute of Electrical and Electronics Engineers2024

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

Los Alamitos2023

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

Jean-Philippe Thiran, Mohammad Saeed Rad, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

In medical image segmentation, supervised deep networks' success comes at the cost of requiring abundant labeled data. While asking domain experts to annotate only one or a few of the cohort's images is feasible, annotating all available images is impracti ...

IEEE COMPUTER SOC2022

OptTTA: Learnable Test-Time Augmentation for Source-Free Medical Image Segmentation Under Domain Shift

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

As distribution shifts are inescapable in realistic clinical scenarios due to inconsistencies in imaging protocols, scanner vendors, and across different centers, well-trained deep models incur a domain generalization problem in unseen environments. Despit ...

PMLR2022

SOoD: Self-Supervised Out-of-Distribution Detection Under Domain Shift for Multi-Class Colorectal Cancer Tissue Types

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar

The goal of out-of-distribution (OoD) detection is to identify unseen categories of inputs different from those seen during training, which is an important requirement for the safe deployment of deep neural networks in computational pathology. Additionally ...

IEEE COMPUTER SOC2021

Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

Jean-Philippe Thiran, Manana Lortkipanidze, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar, Devavrat Tomar

Despite the successes of deep neural networks on many challenging vision tasks, they often fail to generalize to new test domains that are not distributed identically to the training data. The domain adaptation becomes more challenging for cross-modality m ...

Institute of Electrical and Electronics Engineers2021

SALAD: Self-Supervised Aggregation Learning for Anomaly Detection on X-Rays

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Seyedbehzad Bozorgtabar

2020

G2-VER: Geometry Guided Model Ensemble for Video-based Facial Expression Recognition

Jean-Philippe Thiran, Saleh Baghersalimi, Tanguy Albrici, Guillaume Marc Georges Vray, Hazim Kemal Ekenel

This paper addresses the problem of automatic facial expression recognition in videos, where the goal is to predict discrete emotion labels best describing the emotions expressed in short video clips. Building on a pre-trained convolutional neural network ...

IEEE2019