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Scatter signatures in SFDI data enable breast surgical margin delineation via ensemble learning

Abstract: Margin assessment in gross pathology is becoming feasible as various explanatory deep learning-powered methods are able to obtain models for macroscopic textural information, tissue microstructure, and local surface optical properties. Unfortunately, each different method seems to lack enough diagnostic power to perform an adequate classification on its own. This work proposes using several separately trained deep convolutional networks, and averaging their responses, in order to achieve a better margin assessment. Qualitative leave-one-out cross-validation results are discussed for a cohort of 70 samples.

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Authorship: Pardo A., Streeter S.S., Maloney B.W., López-Higuera J.M., Pogue B.W., Conde O.M.,

Congress: Biomedical Applications of Light Scattering (10ª : 2020 : San Francisco)

Publisher: SPIE Society of Photo-Optical Instrumentation Engineers

Publication date: 21/02/2020

No. of pages: 10

Publication type: Conference object

DOI: 10.1117/12.2546945

ISSN: 0277-786X,1996-756X

Spanish project: FIS2010-19860 ; TEC2016-76021-C2-2-R

Publication Url: https://doi.org/10.1117/12.2546945

UCrea Repository Read publication

Authorship

ARTURO PARDO FRANCO

STREETER, SAMUEL S.

MALONEY, BENJAMIN W.

JOSE MIGUEL LOPEZ HIGUERA

BRIAN W. POGUE

OLGA MARIA CONDE PORTILLA

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