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.
Zhu et al. REF extracted the high-level feature representation by using the convolution neural network for prostate cancer detection; the experimental results showed that the proposed method achieved an averaged sensitivity of 91.51% and specificity of 88.47%.
MRI‐based prostate cancer detection with high‐level representation and hierarchical classification
Medical Physics
Physics Medicine