title: MRI-based prostate cancer detection with high-level representation and hierarchical classification.
abstract: OBJECTIVE
  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.
authors.affiliation: Computer Center, Nanjing University of Aeronautics & Astronautics, Jiangsu, China.
authors.forename: Yulian
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, USA.
authors.forename: Mingxia
authors.initials: M
authors.lastname: Liu
authors.affiliation: School of Science, Nanjing University of Science and Technology, Jiangsu, China.
authors.forename: Chunjun
authors.initials: C
authors.lastname: Qian
authors.affiliation: Department of Radiology, Section of Urology, University of Chicago, Chicago, IL, USA.
authors.forename: Ambereen
authors.initials: A
authors.lastname: Yousuf
authors.affiliation: Department of Radiology, Section of Urology, University of Chicago, Chicago, IL, USA.
authors.forename: Aytekin
authors.initials: A
authors.lastname: Oto
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, USA.|Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Korea.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D066264:Datasets as Topic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D016571:Neural Networks, Computer; D011467:Prostate / Q000000981:diagnostic imaging*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D012680:Sensitivity and Specificity
keywords: deep learning; hierarchical classification; magnetic resonance imaging (MRI); prostate cancer detection; random forest