16 citations · 25 across the 9 of their papers we have counts for
8 papers · 1 filter
Look What the Probes Dragged In! Real-World Chest X-ray Shortcuts in MedCLIP
Nikolette Pedersen, Regitze Sydendal, Veronika Cheplygina +1
Vision-language models, such as contrastive language-image pre-training (CLIP)-based approaches, have reached state-of-the-art (SOTA) results in medical artificial intelligence. Ho…
Dataset Diversity Metrics and Impact on Classification Models
Théo Sourget, Niclas Claßen, Jack Junchi Xu +2
The diversity of training datasets is usually perceived as an important aspect to obtain a robust model. However, the definition of diversity is often not defined or differs across…
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,…
In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
Amelia Jiménez-Sánchez, Natalia-Rozalia Avlona, Sarah de Boer +26
Datasets play a critical role in medical imaging research, yet issues such as label quality, shortcuts, and metadata are often overlooked. This lack of attention may harm the gener…
Mask of truth: model sensitivity to unexpected regions of medical images
Théo Sourget, Michelle Hestbek-Møller, Amelia Jiménez-Sánchez +2
The development of larger models for medical image analysis has led to increased performance. However, it also affected our ability to explain and validate model decisions. Models…
Detection Transformer for Teeth Detection, Segmentation, and Numbering in Oral Rare Diseases: Focus on Data Augmentation and Inpainting Techniques
Hocine Kadi, Théo Sourget, Marzena Kawczynski +3
In this work, we focused on deep learning image processing in the context of oral rare diseases, which pose challenges due to limited data availability. A crucial step involves tee…