Demystifying Brain Tumour Segmentation Networks: Interpretability and
Uncertainty Analysis

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Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis

3 September 2019

Ganapathy Krishnamurthi

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Papers citing "Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty Analysis"

9 / 9 papers shown

Title
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge Spyridon Bakas M. Reyes Andras Jakab Stefan Bauer Markus Rempfler ... Jayashree Kalpathy-Cramer Keyvan Farahani Christos Davatzikos Koen van Leemput Bjoern Menze 141 1,638 0 05 Nov 2018
Visual Interpretability for Deep Learning: a Survey Quanshi Zhang Song-Chun Zhu FaML HAI 144 822 0 02 Feb 2018
What do we need to build explainable AI systems for the medical domain? Andreas Holzinger Chris Biemann C. Pattichis D. Kell 74 693 0 28 Dec 2017
Network Dissection: Quantifying Interpretability of Deep Visual Representations David Bau Bolei Zhou A. Khosla A. Oliva Antonio Torralba MILM FAtt 158 1,526 1 19 Apr 2017
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach Xiao Yang Roland Kwitt Martin Styner Marc Niethammer 73 597 0 31 Mar 2017
Demystifying Neural Style Transfer Yanghao Li Naiyan Wang Jiaying Liu Xiaodi Hou 92 521 0 04 Jan 2017
The Importance of Skip Connections in Biomedical Image Segmentation M. Drozdzal Eugene Vorontsov Gabriel Chartrand Samuel Kadoury C. Pal MedIm 88 1,047 0 14 Aug 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 863 9,353 0 06 Jun 2015
Object Detectors Emerge in Deep Scene CNNs Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba ObjD 162 1,283 0 22 Dec 2014

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