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BIOPSY-GUIDED LEARNING WITH DEEP CONVOLUTIONAL NEURAL NETWORKS FOR PROSTATE CANCER DETECTION ON MULTIPARAMETRIC MRI

文献类型： 会议论文

第一作者： Yohannes Tsehay

作者： Yohannes Tsehay 1 ; Nathan Lay 1 ; Xiaosong Wang 1 ; Jin Tae Kwak 1 ; Baris Turkbey 2 ; Peter Choyke 2 ; Peter Pinto 2 ; Brad Wood 3 ; Ronald M. Summers 1 ;

作者机构： 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Science, National Institute of Health, Clinical Center

2.Urologic Oncology Branch, National Cancer Institute, National Institute of Health

3.Center for Interventional Oncology, National Cancer Institute, National Institute of Health

关键词： Biopsy Database;Prostate;Holistically-nested Edge Detection;Computer-Aided Detection;Prostate-CAD;Radiology

会议名称： IEEE International Symposium on Biomedical Imaging

主办单位：

页码： 642-645

摘要： Prostate Cancer (PCa) is highly prevalent and is the second most common cause of cancer-related deaths in men. Multiparametric MRI (mpMRI) is robust in detecting PCa. We developed a weakly supervised computer-aided detection (CAD) system that uses biopsy points to learn to identify PCa on mpMRI. Our CAD system, which is based on a deep convolutional neural network architecture, yielded an area under the curve (AUC) of 0.903±0.009 on a receiver operation characteristic (ROC) curve computed on 10 different models in a 10 fold cross-validation. 9 of the 10 ROCs were statistically significantly different from a competing support vector machine based CAD, which yielded a 0.86 AUC when tested on the same dataset (α=0.05). Furthermore, our CAD system proved to be more robust in detecting high-grade transition zone lesions.

分类号： TP391.41-53

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