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A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI.

, , , , , and . Comput. Medical Imaging Graph., (2015)

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Prostate cancer detection from model-free T1-weighted time series and diffusion imaging., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9414 of SPIE Proceedings, page 94142X. SPIE, (2015)Multi-parametric 3D Quantitative Ultrasound Vibro-Elastography Imaging for Detecting Palpable Prostate Tumors., , , , , , , , , and 2 other author(s). MICCAI (1), volume 8673 of Lecture Notes in Computer Science, page 561-568. Springer, (2014)Multimodal classification of prostate tissue: a feasibility study on combining multiparametric MRI and ultrasound., , , , , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9414 of SPIE Proceedings, page 94141B. SPIE, (2015)Registration of Whole-Mount Histology and Volumetric Imaging of the Prostate Using Particle Filtering., , , , , , and . IEEE Trans. Med. Imaging, 33 (8): 1601-1613 (2014)Ultrasound RF time series for tissue typing: first in vivo clinical results., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 8670 of SPIE Proceedings, page 86701I. SPIE, (2013)A data-driven approach to prostate cancer detection from dynamic contrast enhanced MRI., , , , , and . Comput. Medical Imaging Graph., (2015)Improved parameter extraction and classification for dynamic contrast enhanced MRI of prostate., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9035 of SPIE Proceedings, page 903511. SPIE, (2014)