527 citations · 2.3k across the 108 of their papers we have counts for
9 papers · 2 filters
A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models
Julio Silva-Rodríguez, Sina Hajimiri, Ismail Ben Ayed +1
Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples. While signific…
Anatomically-aware Uncertainty for Semi-supervised Image Segmentation
Sukesh Adiga, Jose Dolz, Herve Lombaert
Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image segmentation by leveraging unlabeled data. A prominent way to exploit unlabeled data is to…
A Foundation Language-Image Model of the Retina (FLAIR): Encoding Expert Knowledge in Text Supervision
Julio Silva-Rodríguez, Hadi Chakor, Riadh Kobbi +2
Foundation vision-language models are currently transforming computer vision, and are on the rise in medical imaging fueled by their very promising generalization capabilities. How…
MoP-CLIP: A Mixture of Prompt-Tuned CLIP Models for Domain Incremental Learning
Julien Nicolas, Florent Chiaroni, Imtiaz Ziko +3
Despite the recent progress in incremental learning, addressing catastrophic forgetting under distributional drift is still an open and important problem. Indeed, while state-of-th…
Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic Segmentation
Balamurali Murugesan, Rukhshanda Hussain, Rajarshi Bhattacharya +2
Recently, CLIP-based approaches have exhibited remarkable performance on generalization and few-shot learning tasks, fueled by the power of contrastive language-vision pre-training…
Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation
Bach Kim, Jose Dolz, Pierre-Marc Jodoin +1
Privacy protection in medical data is a legitimate obstacle for centralized machine learning applications. Here, we propose a client-server image segmentation system which allows f…
Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation
Julio Silva-Rodríguez, Jose Dolz, Ismail Ben Ayed
The recent popularity of foundation models and the pre-train-and-adapt paradigm, where a large-scale model is transferred to downstream tasks, is gaining attention for volumetric m…
Trust your neighbours: Penalty-based constraints for model calibration
Balamurali Murugesan, Sukesh Adiga, Bingyuan Liu +3
Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain. While recent literature on calib…
Harmonizing Flows: Unsupervised MR harmonization based on normalizing flows
Farzad Beizaee, Christian Desrosiers, Gregory A. Lodygensky +1
In this paper, we propose an unsupervised framework based on normalizing flows that harmonizes MR images to mimic the distribution of the source domain. The proposed framework cons…