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11 March 2015 Prostate cancer diagnosis using quantitative phase imaging and machine learning algorithms
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Proceedings Volume 9336, Quantitative Phase Imaging; 933619 (2015) https://doi.org/10.1117/12.2080321
Event: SPIE BiOS, 2015, San Francisco, California, United States
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tan H. Nguyen, Shamira Sridharan, Virgilia Macias, Andre K. Balla, Minh N. Do, and Gabriel Popescu "Prostate cancer diagnosis using quantitative phase imaging and machine learning algorithms", Proc. SPIE 9336, Quantitative Phase Imaging, 933619 (11 March 2015); https://doi.org/10.1117/12.2080321
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