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Journal of Medical Imaging

Detection of prostate cancer in multiparametric MRI using random forest with instance weighting
Author(s): Nathan S. Lay; Yohannes Tsehay; Matthew D. Greer; Baris Turkbey; Jin Tae Kwak; Peter L. Choyke; Peter Pinto; Bradford J. Wood; Ronald M. Summers
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Paper Abstract

A prostate computer-aided diagnosis (CAD) based on random forest to detect prostate cancer using a combination of spatial, intensity, and texture features extracted from three sequences, T2W, ADC, and B2000 images, is proposed. The random forest training considers instance-level weighting for equal treatment of small and large cancerous lesions as well as small and large prostate backgrounds. Two other approaches, based on an AutoContext pipeline intended to make better use of sequence-specific patterns, were considered. One pipeline uses random forest on individual sequences while the other uses an image filter described to produce probability map-like images. These were compared to a previously published CAD approach based on support vector machine (SVM) evaluated on the same data. The random forest, features, sampling strategy, and instance-level weighting improve prostate cancer detection performance [area under the curve (AUC) 0.93] in comparison to SVM (AUC 0.86) on the same test data. Using a simple image filtering technique as a first-stage detector to highlight likely regions of prostate cancer helps with learning stability over using a learning-based approach owing to visibility and ambiguity of annotations in each sequence.

Paper Details

Date Published: 12 June 2017
PDF: 11 pages
J. Med. Imag. 4(2) 024506 doi: 10.1117/1.JMI.4.2.024506
Published in: Journal of Medical Imaging Volume 4, Issue 2
Show Author Affiliations
Nathan S. Lay, National Institutes of Health (United States)
Yohannes Tsehay, National Institutes of Health (United States)
Matthew D. Greer, National Cancer Institute (United States)
Baris Turkbey, National Cancer Institute (United States)
Jin Tae Kwak, National Institutes of Health (United States)
Peter L. Choyke, National Cancer Institute (United States)
Peter Pinto, National Cancer Institute (United States)
Bradford J. Wood, National Institutes of Health (United States)
Ronald M. Summers, National Institutes of Health (United States)

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