Springer Nature
Browse

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

Download (273.98 kB)
figure
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.

Funding

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

History

Usage metrics

    Arthritis Research & Therapy

    Categories

    Keywords

    Computer visionSystemic sclerosisSclerodermaHistologyModified Rodnan skin scoreOutcomesOutcome measuresDeep neural networkAlexNetQuantitative image features

    Licence

    CC BY + CC0

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC