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MRI‐based prostate cancer detection with high‐level representation and hierarchical classification
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 le...
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Published in: | Medical physics (Lancaster) 2017-03, Vol.44 (3), p.1028-1039 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | 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. |
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ISSN: | 0094-2405 2473-4209 |
DOI: | 10.1002/mp.12116 |