@conference{10902/18323,
year = {2019},
month = {7},
url = {http://hdl.handle.net/10902/18323},
abstract = {Extracting pathology information embedded within surface optical properties in Spatial Frequency Domain Imaging (SFDI) datasets is still a rather cumbersome nonlinear translation problem, mainly constrained by intrasample and interpatient variability, as well as dataset size. The B-variational autoencoder (B-VAE) is a rather novel dimensionality reduction technique where a tractable set of latent low-dimensional embeddings can be obtained from a given dataset. These embeddings can then be sampled to synthesize new data, providing further insight into pathology variability as well as differentiability in terms of optical properties. Its applications for data classification and breast margin delineation are also discussed.},
organization = {Research reported in this manuscript was funded by PhD grant FPU016/05705 (Spanish Ministry of Education, Culture and Sports), projects DTS1700055 (FUSIODERM), INNVAL 16/02 (DICUTEN), INNVAL 18/23 (DAPATOO), and TEC201676021C22R (SENSA), as well as cofunded with FEDER funds.},
publisher = {SPIE Society of Photo-Optical Instrumentation Engineers-},
publisher = {The Optical Society (OSA)},
publisher = {Arturo Pardo, José M. López-Higuera, Brian W. Pogue, Olga M. Conde, "Deep variational autoencoders for breast cancer tissue modeling and synthesis in SFDI," in European Conference on Biomedical Optics: Diffuse Optical Spectroscopy and Imaging VII, edited by Hamid Dehghani, Heidrun Wabnitz,  Vol. 11074 of Proceedings of SPIE-OSA Biomedical Optics, 110741G, (2019)},
title = {Deep variational autoencoders for breast cancer tissue modeling and synthesis in SFDI},
author = {Pardo Franco, Arturo and López Higuera, José Miguel and Pogue, Brian W. and Conde Portilla, Olga María},
}