Assessment of CT image reconstruction parameters on radiomic features in a lung cancer screening cohort: the PROSPR study. - Babak Haghighi; Peter B. Noël; Eric A. Cohen; Lauren Pantalone; Anil Vachani; Katharine A. Rendle; Jocelyn Wainwright; Chelsea Saia; Eduardo Jose Mortani Barbosa Jr.; Despina Kontos (Computer-Aided Diagnosis, 2020)

Machine Learning Automatically Detects COVID-19 using Chest CTs in a Large Multicenter Cohort. - Bogdan Georgescu; Shikha Chaganti; Gorka Bastarrika Aleman; Eduardo Jose Mortani Barbosa Jr.; Jordi Broncano Cabrero; Guillaume Chabin; Thomas Flohr 001; Philippe Grenier; Sasa Grbic; Nakul Gupta; François Mellot; Savvas Nicolaou; et al. (CoRR, 2020)

Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth. - Eduardo Jose Mortani Barbosa Jr.; Warren B. Gefter; Rochelle Yang; Florin C. Ghesu; Siqi Liu 001; Boris Mailhé; Awais Mansoor; Sasa Grbic; Sebastian Piat; Guillaume Chabin; R. S. Vishwanath; Abishek Balachandran; et al. (CoRR, 2020)

Quantitative CT texture-based method to predict diagnosis and prognosis of fibrosing interstitial lung disease patterns. - Babak Haghighi; Warren B. Gefter; Lauren Pantalone; Despina Kontos; Eduardo Jose Mortani Barbosa Jr. (CoRR, 2022)

A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images. - Chunrui Zou; Walter C. Mankowski; Lauren Pantalone; Shefali Setia Verma; Eduardo Jose Mortani Barbosa Jr.; Tessa S. Cook; Peter B. Noël; Erica L. Carpenter; Jeffrey C. Thompson; Russell T. Shinohara; Sharyn Katz; Despina Kontos (Computer-Aided Diagnosis, 2023)