High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models
- 31 August 2010
- journal article
- Published by Elsevier BV in Medical Image Analysis
- Vol. 14 (4), 617-629
- https://doi.org/10.1016/j.media.2010.04.007
Abstract
No abstract availableKeywords
This publication has 30 references indexed in Scilit:
- Systems Pathology Approach for the Prediction of Prostate Cancer Progression After Radical ProstatectomyJournal of Clinical Oncology, 2008
- Improved prediction of prostate cancer recurrence through systems pathologyJCI Insight, 2007
- An image analysis approach for automatic malignancy determination of prostate pathological imagesCytometry Part B: Clinical Cytometry, 2007
- How Well Does the Gleason Score Predict Prostate Cancer Death? A 20-Year Followup of a Population Based Cohort in SwedenJournal of Urology, 2006
- Gleason grading of prostate cancer in needle biopsies or radical prostatectomy specimens: contemporary approach, current clinical significance and sources of pathology discrepanciesBJU International, 2005
- Strong markov random field modelIeee Transactions On Pattern Analysis and Machine Intelligence, 2004
- Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithmIEEE Transactions on Medical Imaging, 2001
- Region growing: a new approachIEEE Transactions on Image Processing, 1998
- Texture synthesis via a noncausal nonparametric multiscale Markov random fieldIEEE Transactions on Image Processing, 1998
- Unsupervised image restoration and edge location using compound Gauss-Markov random fields and the MDL principleIEEE Transactions on Image Processing, 1997