Publications

Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes

Featured Publications /

Draelos RL, Dov D, Mazurowski MA, Lo JY, Henao R, Rubin GD, Carin L.
Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.
Med Image Anal. .2021;67:101857. Epub 2020/11/01. PMC7726032. [Foundation]
https://doi.org/10.1016/j.media.2020.101857

Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes Read More »

Minimum perceivable size difference: how well can radiologists visually detect a change in lung nodule size from CT images?

Recent Publications /

Solomon J, Ebner L, Christe A, Peters A, Munz J, Löbelenz L, Klaus J, Richards T, Samei E, Roos JE.
Minimum perceivable size difference: how well can radiologists visually detect a change in lung nodule size from CT images?
Eur Radiol. .2021;31(4):1947-55. Epub 2020/10/01. [Foundation]
https://doi.org/10.1007/s00330-020-07326-2

Minimum perceivable size difference: how well can radiologists visually detect a change in lung nodule size from CT images? Read More »

Quantification of Minimum Detectable Difference in Radiomics Features Across Lesions and CT Imaging Conditions

Recent Publications /

Hoye J, Solomon JB, Sauer TJ, Samei E.
Quantification of Minimum Detectable Difference in Radiomics Features Across Lesions and CT Imaging Conditions.
Acad Radiol. .2020. Epub 2020/08/24. PMC7895859. [Foundation]
https://doi.org/10.1016/j.acra.2020.07.029

Quantification of Minimum Detectable Difference in Radiomics Features Across Lesions and CT Imaging Conditions Read More »

Virtual clinical trial for quantifying the effects of beam collimation and pitch on image quality in computed tomography

Recent Publications /

Abadi E, Segars WP, Harrawood B, Sharma S, Kapadia A, Samei E.
Virtual clinical trial for quantifying the effects of beam collimation and pitch on image quality in computed tomography.
J Med Imaging (Bellingham). .2020;7(4):042806. Epub 2020/06/09. PMC7262564. [Foundation]
https://doi.org/10.1117/1.Jmi.7.4.042806

Virtual clinical trial for quantifying the effects of beam collimation and pitch on image quality in computed tomography Read More »

Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features

Recent Publications /

Saha A, Tushar F, Faryna K, D”Anniballe V, Hou R, Mazurowski M, Rubin G, Lo J.
Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features.
SPIE Medical Imaging. .2020;11314. [Foundation]
https://doi.org/10.1117/12.2550857

Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features Read More »

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