Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT. - Frederic Commandeur; Markus Goeller; Julián Betancur; Sebastien Cadet; Mhairi Doris; Xi Chen 053; Daniel S. Berman; Piotr J. Slomka; Balaji Tamarappoo; Damini Dey (IEEE Trans. Medical Imaging, 2018)

Deep learning-based stenosis quantification from coronary CT angiography. - Youngtaek Hong; Frederic Commandeur; Sebastien Cadet; Markus Goeller; Mhairi Doris; Xi Chen 053; Jacek Kwiecinski; Daniel S. Berman; Piotr J. Slomka; Hyuk-Jae Chang; Damini Dey (Image Processing, 2019)

Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks. - Kajetan Grodecki; Aditya Killekar; Andrew Lin; Sebastien Cadet; Priscilla McElhinney; Aryabod Razipour; Cato Chan; Barry D. Pressman; Peter Julien; Judit Simon; Pál Maurovich-Horvat; Nicola Gaibazzi; et al. (CoRR, 2021)

COVID-19 lesion segmentation using convolutional LSTM for self-attention. - Aditya Killekar; Kajetan Grodecki; Andrew Lin; Sebastien Cadet; Priscilla McElhinney; Aryabod Razipour; Cato Chan; Barry D. Pressman; Peter Julien; Peter Chen; Judit Simon; Nitesh Nerlekar; et al. (Image Processing, 2022)

Direct head-to-head comparison of convolutional long short-term memory and transformer networks for artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary CT angiography. - Nipun Manral; Andrew Lin; Caroline Park; Priscilla McElhinney; Aditya Killekar; Hidenari Matsumoto; Jacek Kwiecinski; Konrad Pieszko; Aryabod Razipour; Kajetan Grodecki; Mhairi Doris; Alan C. Kwan; et al. (Image Processing, 2023)