Extracting the high-level feature representation by using deep neural networks for detection of prostate cancer, and then based on high-level feature representation for constructing hierarchical classification to refine the detection results.
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High-level feature representation is first learned by a deep learning network, where multi-parametric MR images are used as the input data. Then, based on the learned high-level features, a hierarchical classification method is developed, where multiple random forest classifiers are iteratively constructed to refine the detection results of prostate cancer.
The experiments were carried on 21 real patient subjects, and the proposed method achieves an averaged section-based evaluation (SBE) of 89.90%, an averaged sensitivity of 91.51%, and an averaged specificity of 88.47%.
The high-level features learned from our proposed method can achieve better performance than the conventional handcrafted features (e.g., LBP and Haar-like features) in detecting prostate cancer regions, also the context features obtained from the proposed hierarchical classification approach are effective in refining cancer detection result. This article is protected by copyright. All rights reserved.
Medical physics. 2017 Jan 20 [Epub ahead of print]
Yulian Zhu, Li Wang, Mingxia Liu, Chunjun Qian, Ambereen Yousuf, Aytekin Oto, Dinggang Shen
Computer Center, Nanjing University of Aeronautics & Astronautics, Jiangsu, China., Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA., School of Science, Nanjing University of Science and Technology, Jiangsu, China., Department of Radiology, Section of Urology, University of Chicago, IL, USA.