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Proceedings Paper

Scatter signatures in SFDI data enable breast surgical margin delineation via ensemble learning
Author(s): Arturo Pardo; Samuel S. Streeter; Benjamin W. Maloney; José M. López-Higuera; Brian W. Pogue; Olga M. Conde
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Paper 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.

Paper Details

Date Published: 21 February 2020
PDF: 10 pages
Proc. SPIE 11253, Biomedical Applications of Light Scattering X, 112530K (21 February 2020); doi: 10.1117/12.2546945
Show Author Affiliations
Arturo Pardo, Univ. de Cantabria (Spain)
Instituto de Investigación Valdecilla (Spain)
Samuel S. Streeter, Thayer School of Engineering at Dartmouth (United States)
Benjamin W. Maloney, Thayer School of Engineering at Dartmouth (United States)
José M. López-Higuera, Univ. de Cantabria (Spain)
Instituto de Investigación Valdecilla (Spain)
CIBER-BBN (Spain)
Brian W. Pogue, Thayer School of Engineering at Dartmouth (United States)
Olga M. Conde, Univ. de Cantabria (Spain)
Instituto de Investigación Valdecilla (Spain)
CIBER-BBN (Spain)


Published in SPIE Proceedings Vol. 11253:
Biomedical Applications of Light Scattering X
Adam Wax; Vadim Backman, Editor(s)

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