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Proceedings Paper

AI-driven imaging biomarkers for sensory cue integration during melanoma screening (Conference Presentation)
Author(s): Daniel S. Gareau; Charles Vrattos; James Browning; Samantha R Lish; Benjamin Firester; James G. Krueger

Paper Abstract

Early diagnosis of melanomas is the most effective means of improving melanoma prognosis. We can arm the non-expert screeners with artificial intelligence but most artificial intelligence methods are somewhat impractical in a clinical setting given the lack of transparency. To provide a quantitative and algorithmic approach to lesion diagnosis while maintaining transparency, and to supplement the clinician rather than replace them, our digital analysis provides visual features, or, “imaging biomarkers” that can both be used in machine learning and visualized too.

Paper Details

Date Published: 9 March 2020
Proc. SPIE 11230, Optics and Biophotonics in Low-Resource Settings VI, 112300U (9 March 2020); doi: 10.1117/12.2550189
Show Author Affiliations
Daniel S. Gareau, The Rockefeller Univ. (United States)
Charles Vrattos, The Rockefeller Univ. (United States)
James Browning, The Rockefeller Univ. (United States)
Samantha R Lish, The Rockefeller Univ. (United States)
Benjamin Firester, The Rockefeller Univ. (United States)
James G. Krueger, The Rockefeller Univ. (United States)

Published in SPIE Proceedings Vol. 11230:
Optics and Biophotonics in Low-Resource Settings VI
David Levitz; Aydogan Ozcan, Editor(s)

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