3 papers
cs.CV2026
Prompting with the human-touch: evaluating model-sensitivity of foundation models for musculoskeletal CT segmentation
Caroline Magg, Maaike A. ter Wee, Johannes G. G. Dobbe +4
Promptable Foundation Models (FMs), initially introduced for natural image segmentation, have also revolutionized medical image segmentation. The increasing number of models, along…
cs.CV2025
In-hoc Concept Representations to Regularise Deep Learning in Medical Imaging
Valentina Corbetta, Floris Six Dijkstra, Regina Beets-Tan +3
Deep learning models in medical imaging often achieve strong in-distribution performance but struggle to generalise under distribution shifts, frequently relying on spurious correl…
cs.CV2024
Zero-shot capability of SAM-family models for bone segmentation in CT scans
Caroline Magg, Hoel Kervadec, Clara I. Sánchez
The Segment Anything Model (SAM) and similar models build a family of promptable foundation models (FMs) for image and video segmentation. The object of interest is identified usin…