Abstract
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 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 itera-tively 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.
Original language | English |
---|---|
Pages (from-to) | 1028-1039 |
Number of pages | 12 |
Journal | Medical physics |
Volume | 44 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2017 Mar |
Keywords
- deep learning
- hierarchical classification
- magnetic resonance imaging (MRI)
- prostate cancer detection
- random forest
ASJC Scopus subject areas
- Biophysics
- Radiology Nuclear Medicine and imaging