1 citations · 1 across the 5 of their papers we have counts for
6 papers
Unlocking Generalization in Polyp Segmentation with DINO Self-Attention "keys"
Carla Monteiro, Valentina Corbetta, Regina Beets-Tan +2
Automatic polyp segmentation is crucial for improving the clinical identification of colorectal cancer (CRC). While Deep Learning (DL) techniques have been extensively researched f…
Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer
Daniël Boeke, Cedrik Blommestijn, Rebecca N. Wray +8
Recurrence risk estimation in clear cell renal cell carcinoma (ccRCC) is essential for guiding postoperative surveillance and treatment. The Leibovich score remains widely used for…
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…
Towards Case-based Interpretability for Medical Federated Learning
Laura Latorre, Liliana Petrychenko, Regina Beets-Tan +2
We explore deep generative models to generate case-based explanations in a medical federated learning setting. Explaining AI model decisions through case-based interpretability is…
Enhancing Cross-Modal Medical Image Segmentation through Compositionality
Aniek Eijpe, Valentina Corbetta, Kalina Chupetlovska +2
Cross-modal medical image segmentation presents a significant challenge, as different imaging modalities produce images with varying resolutions, contrasts, and appearances of anat…
FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation
Philip Schutte, Valentina Corbetta, Regina Beets-Tan +1
Federated Learning (FL) in Deep Learning (DL)-automated medical image segmentation helps preserving privacy by enabling collaborative model training without sharing patient data. H…