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Proceedings Paper

Prostate cancer diagnosis using quantitative phase imaging and machine learning algorithms
Author(s): Tan H. Nguyen; Shamira Sridharan; Virgilia Macias; Andre K. Balla; Minh N. Do; Gabriel Popescu
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Paper 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.

Paper Details

Date Published: 11 March 2015
PDF: 10 pages
Proc. SPIE 9336, Quantitative Phase Imaging, 933619 (11 March 2015); doi: 10.1117/12.2080321
Show Author Affiliations
Tan H. Nguyen, Univ. of Illinois at Urbana-Champaign (United States)
Shamira Sridharan, Univ. of Illinois at Urbana-Champaign (United States)
Virgilia Macias, Univ. of Chicago (United States)
Andre K. Balla, Univ. of Chicago (United States)
Minh N. Do, Univ. of Illinois at Urbana-Champaign (United States)
Gabriel Popescu, Univ. of Illinois at Urbana-Champaign (United States)

Published in SPIE Proceedings Vol. 9336:
Quantitative Phase Imaging
Gabriel Popescu; YongKeun Park, Editor(s)

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