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Additional file 2 of High-throughput quantitative histology in systemic sclerosis skin disease using computer vision

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posted on 2020-03-15, 04:34 authored by Chase Correia, Seamus Mawe, Shane Lofgren, Roberta G. Marangoni, Jungwha Lee, Rana Saber, Kathleen Aren, Michelle Cheng, Shannon Teaw, Aileen Hoffmann, Isaac Goldberg, Shawn E. Cowper, Purvesh Khatri, Monique Hinchcliff, J. Matthew Mahoney

Additional file 2 Figure S1. Correlation between the Fibrosis Score and the SSc Four-Gene Biomarker. The correlation between the Deep Neural Network-derived Fibrosis Score and three of four genes (that passed filter criteria) from the SSc Four Gene Biomarker are displayed.

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National Institute of Child Health and Human Development National Institute of Arthritis and Musculoskeletal and Skin Diseases U.S. National Library of Medicine National Institutes of Health NIH Scleroderma Research Foundation

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Computer vision Systemic sclerosis Scleroderma Histology Modified Rodnan skin score Outcomes Outcome measures Deep neural network AlexNet Quantitative image features

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