@inproceedings{e062b4725e2c4bfc8ca7b4eeeffb5cfe,
title = "Prostate cancer diagnosis using quantitative phase imaging and machine learning algorithms",
abstract = "We report, for the first time, the use of Quantitative Phase Imaging (QPI) images to perform automatic prostate cancer diagnosis. A machine learning algorithm is implemented to learn textural behaviors of prostate samples imaged under QPI and produce labeled maps of different regions for testing biopsies (e.g. gland, stroma, lumen etc.). From these maps, morphological and textural features are calculated to predict outcomes of the testing samples. Current performance is reported on a dataset of more than 300 cores of various diagnosis results.",
keywords = "Quantitative Phase Imaging, automatic diagnosis, prostate cancer, texton analysis",
author = "Nguyen, {Tan H.} and Shamira Sridharan and Virgilia Macias and Balla, {Andre K.} and Do, {Minh N.} and Gabriel Popescu",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; 1st Conference on Quantitative Phase Imaging, QPI 2015 ; Conference date: 07-02-2015 Through 10-02-2015",
year = "2015",
doi = "10.1117/12.2080321",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "YongKeun Park and Gabriel Popescu",
booktitle = "Quantitative Phase Imaging",
}