1 citations · 1 across the 4 of their papers we have counts for
5 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…
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…