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• W2592069135 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." @default.
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• W2592069135 date "2017-03-03" @default.
• W2592069135 modified "2023-10-18" @default.
• W2592069135 title "Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks" @default.
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• W2592069135 doi "https://doi.org/10.1117/12.2277123" @default.
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