5 papers
Are foundation models for computer vision good conformal predictors?
Leo Fillioux, Julio Silva-RodrÃguez, 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…
Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation
Julio Silva-RodrÃguez, 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…
A Reality Check of Vision-Language Pre-training in Radiology: Have We Progressed Using Text?
Julio Silva-RodrÃguez, Jose Dolz, Ismail Ben Ayed
Vision-language pre-training has recently gained popularity as it allows learning rich feature representations using large-scale data sources. This paradigm has quickly made its wa…
Few-shot Adaptation of Medical Vision-Language Models
Fereshteh Shakeri, Yunshi Huang, Julio Silva-RodrÃguez +4
Integrating image and text data through multi-modal learning has emerged as a new approach in medical imaging research, following its successful deployment in computer vision. Whil…
Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images
Saul Fuster, Farbod Khoraminia, Julio Silva-RodrÃguez +7
We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated pro…