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We hypothesize that probabilistic voxel-level classification of anatomy and malignancy in prostate MRI, although typically posed as near-identical segmentation tasks via U-Nets, require different loss functions for optimal performance due to inherent differences in their clinical objectives.
Prostate Cancer Localization Using Multiparametric MRI based on Semisupervised Techniques With Automated Seed Initialization
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Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework
A. G. Chung, F. Khalvati, M. J. Shafiee, M. A. Haider, and A. Wong · 2015
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New and Established Technology in Focal Ablation of the Prostate: A Systematic Review
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PI-RADS Prostate Imaging – Reporting and Data System: 2015, Version 2
J.C. Weinreb, J.O. Barentsz, P.L. Choyke, and F. Cornud · 2016
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SGDR: Stochastic Gradient Descent with Warm Restarts
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A.B. Rosenkrantz, L.A. Ginocchio, D. Cornfeld, and A.T. Froemming · 2016
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Adversarial Networks for the Detection of Aggressive Prostate Cancer, 2017
S. Kohl, D. Bonekamp, H. P. Schlemmer, and K. Maier-Hein et al · 2017
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Focal Loss for Dense Object Detection
T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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Family of Boundary Overlap Metrics for the Evaluation of Medical Image Segmentation
V. Yeghiazaryan and I. D. Voiculescu · 2018
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PROSTATEx Challenges for Computerized Classification of Prostate Lesions from Multiparametric Magnetic Resonance Images
S. G. Armato and H. Huisman and K. Drukker and L. Hadjiiski et al · 2018
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A Probabilistic U-Net for Segmentation of Ambiguous Images
S. A. A. Kohl, B. Romera-Paredes, C. Meyer, and O. Ronneberger et al · 2018
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Accurate Uncertainties for Deep Learning Using Calibrated Regression
V. Kuleshov, N. Fenner, and S. Ermon · 2018
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Supervised uncertainty quantification for segmentation with multiple annotations
S. Hu, D. Worrall, S. Knegt, and M. Welling et al · 2019
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Joint Prostate Cancer Detection and Gleason Score Prediction in mp-MRI via FocalNet
R. Cao, A. Mohammadian Bajgiran, S. Afshari Mirak, S. Shakeri, X. Zhong, D. Enzmann, S. Raman, and K. Sung · 2019
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Prostate Cancer Detection and Segmentation in Multi-parametric MRI via CNN and Conditional Random Field
R. Cao, X. Zhong, S. Shakeri, and K. Sung et al · 2019
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Cancer Treatment and Survivorship Statistics, 2019
Confidence calibration and predictive uncertainty estimation for deep medical image segmentation
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Calibrating deep neural networks using focal loss
J. Mukhoti, V. Kulharia, A. Sanyal, and P. K. Dokania et al · 2020
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Multiparametric Magnetic Resonance Imaging for the Detection of Clinically Significant Prostate Cancer: What Urologists Need to Know. Part 2: Interpretation
B. Israël, M. van der Leest, M. Sedelaar, A.R. Padhani, P. Zámecnik, and J.O. Barentsz · 2020
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MRI-Targeted or Standard Biopsy in Prostate Cancer Screening
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Classification of Cancer at Prostate MRI: Deep Learning versus Clinical PI-RADS Assessment
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Combined Use of Prostate-specific Antigen Density and Magnetic Resonance Imaging for Prostate Biopsy Decision Planning: A Retrospective Multi-institutional Study Using the Prostate Magnetic Resonance Imaging Outcome Database (PROMOD)
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