Publications

Optimization of imaging conditions in pediatric dynamic chest radiography: a virtual imaging trial. SPIE Medical Imaging

Tanaka R, Segars W, Abadi E, Minami S, Samei E.
Optimization of imaging conditions in pediatric dynamic chest radiography: a virtual imaging trial.
SPIE Medical Imaging. 2022;12031.
https://doi.org/10.1117/12.2612720

Optimization of imaging conditions in pediatric dynamic chest radiography: a virtual imaging trial. SPIE Medical Imaging Read More »

Virtual vs. reality: external validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography

Tushar FI, Abadi E, Sotoudeh-Paima S, Fricks R, Mazurowski M, Segars WP, Samei E, Lo J.
Virtual vs. reality: external validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography.
SPIE Medical Imaging. 2022;12033.

Virtual vs. reality: external validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography Read More »

Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT

Tushar FI, D’Anniballe V, Rubin G, Samei E, Lo J.
Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT.
SPIE Medical Imaging. 2022;12033
https://doi.org/10.1117/12.2612700

Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT Read More »

A truth-based primal-dual learning approach to reconstruct CT images utilizing the virtual imaging trial platform

Zarei M, Sotoudeh-Paima S, Abadi E, Samei E.
A truth-based primal-dual learning approach to reconstruct CT images utilizing the virtual imaging trial platform.
SPIE Medical Imaging. 2022;12031.
https://doi.org/10.1117/12.2613168

A truth-based primal-dual learning approach to reconstruct CT images utilizing the virtual imaging trial platform Read More »

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.

D’Anniballe VM, Tushar FI, Faryna K, Han S, Mazurowski MA, Rubin GD, Lo JY.
Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.
BMC Medical Informatics and Decision Making. 2022;22(1):102. 2022.
https://doi.org/10.1186/s12911-022-01843-4

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning. Read More »

Development of scalable lymphatic system in the 4D XCAT phantom: application to quantitative evaluation of lymphoma PET segmentations

Fedrigo R, Segars WP, Martineau P, Gowdy C, Bloise I, Uribe CF, Rahmim A.
Development of scalable lymphatic system in the 4D XCAT phantom: application to quantitative evaluation of lymphoma PET segmentations.
Medical Physics. 2022.
https://doi.org/10.1002/mp.15963

Development of scalable lymphatic system in the 4D XCAT phantom: application to quantitative evaluation of lymphoma PET segmentations Read More »