3 papers
cs.CV2025
Evaluating the Explainability of Vision Transformers in Medical Imaging
Leili Barekatain, Ben Glocker
Understanding model decisions is crucial in medical imaging, where interpretability directly impacts clinical trust and adoption. Vision Transformers (ViTs) have demonstrated state…
q-bio.TO2025
A Comprehensive Pipeline for Aortic Segmentation and Shape Analysis
Nairouz Shehata, Amr Elsawy, Mohamed Nagy +6
Aortic shape analysis plays a key role in cardiovascular diagnostics, treatment planning, and understanding disease progression. We present a robust, fully automated pipeline for a…
cs.CV2025
Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation
Theodore Barfoot, Luis Garcia-Peraza-Herrera, Ben Glocker +1
Deep neural networks for medical image segmentation often produce overconfident results misaligned with empirical observations. Such miscalibration, challenges their clinical trans…