Elsa D. Angelini is the lead Senior Data Scientist for the newly created Institute of Translational Medicine and Therapeutics (ITMAT) within NIHR Imperial Biomedical Research Centre (BRC).
She is also affiliated with the Heffner Biomedical Imaging Laboratory at Columbia University and the Department of Image & Signal Processing at Telecom Paristech (Associate Professor / on leave).
She is a Senior Member of IEEE, and has co-authored over 130 peer-reviewed articles. She is the current 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 is co-chair of the SPIE MI Conference on Image Processing since 2016 (in the program committee since 2011). She has served several EMBC conferences. She was on the organizing committee of MICCAI’08 and ISBI’08, she was he general chair of ISBI’15 in Brooklyn NY and she is the current chair of the ISBI Steering Committee. 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., 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
Angelini E, Dahan S, Shah A, 2019, Unravelling Machine Learning - Insights in Respiratory Medicine., Eur Respir J
et al., 2019, Association Between Long-term Exposure to Ambient Air Pollution and Change in Quantitatively Assessed Emphysema and Lung Function., Jama, Vol:322, Pages:546-556
et al., 2019, Transfer learning from partial annotations for whole brain segmentation, International Workshop on Medical Image Learning with Less Labels and Imperfect Data, arXiv