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Deep variational autoencoders for breast cancer tissue modeling and synthesis in SFDI

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.

Other conference communications or articles related to authors from the University of Cantabria

 Authorship: Pardo A., López-Higuera J., Pogue B., Conde O.,

 Congress: European Conference on Biomedical Optics: ECBO (2019 : Múnich)

 Publisher: The Optical Society (OSA) - SPIE Society of Photo-Optical Instrumentation Engineers

 Publication date: 11/07/2019

 No. of pages: 3

 Publication type: Conference object

 DOI: 10.1117/12.2527142

 ISSN: 0277-786X,1996-756X

 Spanish project: TEC2016-76021-C2-2-R

 Publication Url: