activity
20172025
most citedImproving the generalizability of convolutional neural network-based segmentation on CMR images

127 citations · 145 across the 5 of their papers we have counts for

collaborators

5 papers

q-bio.OT2025

Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities

Daniele Malpetti, Marco Scutari, Francesco Gualdi +6

Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. This paper provid…

eess.IV2019★ 127 cited

Improving the generalizability of convolutional neural network-based segmentation on CMR images

Chen Chen, Wenjia Bai, Rhodri H. Davies +13

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR…

cs.CV2019★ 1 cited

Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study

Robert Robinson, Vanya V. Valindria, Wenjia Bai +19

Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic proc…

cs.CV2018★ 13 cited

Real-time Prediction of Segmentation Quality

Robert Robinson, Ozan Oktay, Wenjia Bai +17

Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due…

cs.CV2017★ 4 cited

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Wenjia Bai, Matthew Sinclair, Giacomo Tarroni +20

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accura…