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Abstract #0821

End-to-end Deep Learning Strategy To Segment Prostate Cancer From Multi-parametric MR Images

David Hoar1, Peter Lee2, Alessandro Guida3, Steven Patterson3, Chris Bowen3,4, Jennifer Merrimen5, Cheng Wang5, Ricardo Rendon6, Steven Beyea3,4, and Sharon Elizabeth Clarke3,4
1Department of Electrical and Computer Engineering, Dalhousie University, Halifax, NS, Canada, 2Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada, 3Biomedical Translational Imaging Centre, Nova Scotia Health Authority and IWK Health Centre, Halifax, NS, Canada, 4Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada, 5Pathology, Dalhousie University, Halifax, NS, Canada, 6Urology, Dalhousie University, Halifax, NS, Canada

The purpose of this study was to develop a convolutional neural network (CNN) for dense prediction of prostate cancer using mp-MRI datasets. Baseline CNN outperformed logistic regression and random forest models. Transfer learning and unsupervised pre-training did not significantly improve CNN performance; however, test-time augmentation resulted in significantly higher F1 scores over both baseline CNN and CNN plus either of transfer learning or unsupervised pre-training. The best performing model was CNN with transfer learning and test-time augmentation (F1 score of 0.59, AUPRC of 0.61 and AUROC of 0.93).

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