Name
Title | ||
|---|---|---|
Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks. |
Abstract | ||
|---|---|---|
Prostate cancer (PCa) remains a leading cause of cancer mortality among American men. Multi-parametric magnetic resonance imaging (mpMRI) is widely used to assist with detection of PCa and characterization of its aggressiveness. Computer-aided diagnosis (CADx) of PCa in MRI can be used as clinical decision support system to aid radiologists in interpretation and reporting of mpMRI. We report on the development of a convolution neural network (CNN) model to support CADx in PCa based on the appearance of prostate tissue in mpMRI, conducted as part of the SPIE-AAPM-NCI PROSTATEx challenge. The performance of different combinations of mpMRI inputs to CNN was assessed and the best result was achieved using DWI and DCE-MRI modalities together with the zonal information of the finding. On the test set, the model achieved an area under the receiver operating characteristic curve of 0.80. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1117/12.2277123 | Proceedings of SPIE |
Field | DocType | Volume |
Computer vision,Receiver operating characteristic,Pattern recognition,Convolutional neural network,Computer-aided diagnosis,Prostate cancer,Artificial intelligence,Clinical decision support system,Artificial neural network,Principal component analysis,Magnetic resonance imaging,Physics | Conference | 10134 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
1 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alireza Mehrtash | 1 | 44 | 5.69 |
| Alireza Sedghi | 2 | 12 | 6.80 |
| Mohsen Ghafoorian | 3 | 681 | 27.23 |
| Mehdi Taghipour | 4 | 0 | 0.34 |
| Clare M Tempany | 5 | 629 | 45.11 |
| William M. Wells III | 6 | 5267 | 833.10 |
| Tina Kapur | 7 | 390 | 45.30 |
| Parvin Mousavi | 8 | 366 | 56.95 |
| Purang Abolmaesumi | 9 | 951 | 111.52 |
| Andriy Fedorov | 10 | 171 | 16.54 |