1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data
Valentina Corbetta, Regina Beets-Tan, Wilson Silva
Multi-centre colonoscopy images from various medical centres exhibit distinct complicating factors and overlays that impact the image content, contingent on the specific acquisitio…