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MRI ‐based prostate cancer detection with high‐level representation and hierarchical classification
Author(s) -
Zhu Yulian,
Wang Li,
Liu Mingxia,
Qian Chunjun,
Yousuf Ambereen,
Oto Aytekin,
Shen Dinggang
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12116
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , context (archaeology) , feature (linguistics) , prostate cancer , representation (politics) , random forest , cancer detection , artificial neural network , cancer , medicine , paleontology , linguistics , philosophy , politics , political science , law , biology
Purpose Extracting the high‐level feature representation by using deep neural networks for detection of prostate cancer, and then based on high‐level feature representation constructing hierarchical classification to refine the detection results. Methods High‐level feature representation is first learned by a deep learning network, where multiparametric 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. Results 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%. Conclusions 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.