Elsa D. Angelini is the co-lead of the Data Science Group in Institute of Translational Medicine and Therapeutics (ITMAT) within NIHR Imperial Biomedical Research Centre (BRC).
She is also the co-director of the Heffner Biomedical Imaging Laboratory at Columbia University and is affiliated with the Department of Data-Signal-lmage at Telecom Paris (Associate Professor / on leave). She has co-authored over 140 peer-reviewed articles and has graduated 19 PhD students.
She is a Senior Member of IEEE and was the Vice-President for Technical Activities for IEEE EMBS (2017-19).
She is or has served as an Associate Editor for IEEE T-BME and J-BHI, and on the steering committee of IEEE T-MI. She is or has been an elected member of several boards (EMBS AdCom, ParisTech AdCom, CNRS INS2I Scientific Advisory Board). She has been on the program committee of MICCAI’07-08-11-12, and co-chair (2016-19) of the SPIE Medical Imaging Conference on Image Processing. She was on the organizing committee of MICCAI’08 and ISBI’08, she was he general chair of ISBI’15 in Brooklyn NY and chair of the ISBI Steering Committee (2016-18). She is a member (chair 2013-15) of the EMBS TC on Biomedical Imaging and Image Processing (BIIP) and of the SPS TC on BioImaging and Signal Processing (BISP).
et al., 2020, Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention, Future Generation Computer Systems: the International Journal of Grid Computing: Theory, Methods and Applications, Vol:107, ISSN:0167-739X, Pages:215-228
Angelini E, Dahan S, Shah A, 2019, Unravelling machine learning: insights in respiratory medicine., Eur Respir J, Vol:54
et al., 2019, Vertebral rotation estimation from frontal X-rays using a quasi-automated pedicle detection method., Eur Spine J, Vol:28, Pages:3026-3034
et al., 2019, Quantifying Brain [18F]FDG Uptake Noninvasively by Combining Medical Health Records and Dynamic PET Imaging Data., Ieee J Biomed Health Inform, Vol:23, Pages:2576-2582
et al., 2019, Transfer learning from partial annotations for whole brain segmentation, International Workshop on Medical Image Learning with Less Labels and Imperfect Data