TRD1

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

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

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

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

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

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

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

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

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