Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense
Multi-Label CRFs

Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs

1 March 2017

Urspeter Knecht

Roland Wiest

Papers citing "Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs"

6 / 6 papers shown

Title
Cascaded V-Net using ROI masks for brain tumor segmentation Adrià Casamitjana Hugo J. Kuijf Bjoern Menze M. Reyes Verónica Vilaplana 46 196 0 30 Dec 2018
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields P. Christ M. Elshaer Florian Ettlinger S. Tatavarty Marc Bickel ... Felix O. Hofmann M. D’Anastasi Wieland H. Sommer Seyed-Ahmad Ahmadi Bjoern Menze MedIm 53 633 0 07 Oct 2016
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation Konstantinos Kamnitsas C. Ledig Virginia Newcombe Joanna P. Simpson A. D. Kane David Menon Daniel Rueckert Ben Glocker MedIm 3DV 130 3,053 0 18 Mar 2016
Structured Stochastic Variational Inference Matthew D. Hoffman David M. Blei BDL 85 87 0 16 Apr 2014
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations Tamir Hazan Subhransu Maji Tommi Jaakkola 59 56 0 29 Sep 2013
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials Philipp Krahenbuhl V. Koltun 121 3,452 0 20 Oct 2012

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.