805 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Barts Health NHS TrustGB4 papers
- Health Data Research UKGB3 papers
- Queen Mary University of LondonGB3 papers
- The Alan Turing InstituteGB3 papers
- University College LondonGB3 papers
- British Heart FoundationGB2 papers
- Guy's and St Thomas' NHS Foundation TrustGB2 papers
- Imperial College LondonGB2 papers
- Universitat de BarcelonaES2 papers
- University of OxfordGB2 papers
- University of VeronaIT2 papers
- University of ZakhoIQ2 papers
6 papers
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME
Ahmed Salih, Zahra Raisi-Estabragh, Ilaria Boscolo Galazzo +4
eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communica…
Characterizing the contribution of dependent features in XAI methods
Ahmed Salih, Ilaria Boscolo Galazzo, Zahra Raisi-Estabragh +3
Explainable Artificial Intelligence (XAI) provides tools to help understanding how the machine learning models work and reach a specific outcome. It helps to increase the interpret…
An AI tool for automated analysis of large-scale unstructured clinical cine CMR databases
Jorge Mariscal-Harana, Clint Asher, Vittoria Vergani +9
Artificial intelligence (AI) techniques have been proposed for automating analysis of short axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to aut…
Correspondence on ACMG STATEMENT: ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG) by Miller et al
Kathryn A. McGurk, Sean L. Zheng, Albert Henry +8
We were interested to read the recent update on recommendations for reporting of secondary findings in clinical sequencing1, and the accompanying updated list of genes in which sec…
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…
Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks
Evangelos B. Mazomenos, Kamakshi Bansal, Bruce Martin +3
Transoesophageal echocardiography (TEE) is a valuable diagnostic and monitoring imaging modality. Proper image acquisition is essential for diagnosis, yet current assessment techni…