AI-driven imaging biomarkers for sensory cue integration during melanoma screening (Conference Presentation)

Daniel S. Gareau; Charles Vrattos; James Browning; Samantha R Lish; Benjamin Firester; James G. Krueger

doi:10.1117/12.2550189

9 March 2020 AI-driven imaging biomarkers for sensory cue integration during melanoma screening (Conference Presentation)

Daniel S. Gareau, Charles Vrattos, James Browning, Samantha R Lish, Benjamin Firester, James G. Krueger

Author Affiliations +

Proceedings Volume 11230, Optics and Biophotonics in Low-Resource Settings VI; 112300U (2020) https://doi.org/10.1117/12.2550189
Event: SPIE BiOS, 2020, San Francisco, California, United States

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.

Conference Presentation

Citation Download Citation

Daniel S. Gareau, Charles Vrattos, James Browning, Samantha R Lish, Benjamin Firester, and James G. Krueger "AI-driven imaging biomarkers for sensory cue integration during melanoma screening (Conference Presentation)", Proc. SPIE 11230, Optics and Biophotonics in Low-Resource Settings VI, 112300U (9 March 2020); https://doi.org/10.1117/12.2550189

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