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Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images
- Source :
- IEEE transactions on medical imaging, vol 38, iss 4
- Publication Year :
- 2018
-
Abstract
- Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head and a grading network head. Compared with a single-task model, our multi-task model can provide complementary contextual information, which contributes to better performance. Our model is achieved a state-of-the-art performance in epithelial cells detection and Gleason grading tasks simultaneously. Using fivefold cross-validation, our model is achieved an epithelial cells detection accuracy of 99.07% with an average area under the curve of 0.998. As for Gleason grading, our model is obtained a mean intersection over union of 79.56% and an overall pixel accuracy of 89.40%.
- Subjects :
- Urologic Diseases
Male
Aging
medicine.medical_specialty
Neural Networks
Gleason grading
Convolutional neural network
Article
030218 nuclear medicine & medical imaging
Computer
03 medical and health sciences
Prostate cancer
Computer-Assisted
Engineering
0302 clinical medicine
Prostate
Information and Computing Sciences
Image Interpretation, Computer-Assisted
medicine
Medical imaging
Humans
Electrical and Electronic Engineering
Radiation treatment planning
Image Interpretation
Grading (tumors)
Cancer
Radiological and Ultrasound Technology
business.industry
Histocytochemistry
Prostate Cancer
Prostatic Neoplasms
Image segmentation
Computer-aided diagnosis
medicine.disease
Computer Science Applications
Nuclear Medicine & Medical Imaging
medicine.anatomical_structure
region-based convolutional neural networks
Radiology
Neural Networks, Computer
Neoplasm Grading
business
Software
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on medical imaging, vol 38, iss 4
- Accession number :
- edsair.doi.dedup.....b4cfbc2e3f29dc51687038cec3a8b02f