17 citations · 19 across the 5 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
Leveraging point annotations in segmentation learning with boundary loss
Eva Breznik, Hoel Kervadec, Filip Malmberg +4
This paper investigates the combination of intensity-based distance maps with boundary loss for point-supervised semantic segmentation. By design the boundary loss imposes a strong…
On the dice loss gradient and the ways to mimic it
Hoel Kervadec, Marleen de Bruijne
In the past few years, in the context of fully-supervised semantic segmentation, several losses -- such as cross-entropy and dice -- have emerged as de facto standards to supervise…
Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!
Hoel Kervadec, Houda Bahig, Laurent Letourneau-Guillon +2
Standard losses for training deep segmentation networks could be seen as individual classifications of pixels, instead of supervising the global shape of the predicted segmentation…