@conference{10902/20858,
year = {2020},
month = {2},
url = {http://hdl.handle.net/10902/20858},
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.},
organization = {Spanish Ministry of Science, Innovation and Universities (FIS2010-19860, TEC2016-76021-C2-2-R), Spanish Ministry of Economy, Industry and Competitiveness and Instituto de Salud Carlos III (DTS17-00055, DTS15-
00238), Instituto de Investigación Valdecilla (INNVAL16/02, INNVAL18/23), Spanish Ministry of Education, Culture, and Sports (FPU16/05705).},
publisher = {SPIE Society of Photo-Optical Instrumentation Engineers},
publisher = {Proceedings of SPIE, 2020, 11253, 112530K},
publisher = {Biomedical Applications of Light Scattering X, San Francisco, California, 2020},
title = {Scatter signatures in SFDI data enable breast surgical margin delineation via ensemble learning},
author = {Pardo Franco, Arturo and Streeter, Samuel S. and Maloney, Benjamin W. and López Higuera, José Miguel and Pogue, Brian Wiliam and Conde Portilla, Olga María},
}