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Machine Learning Workflow to Explain Black-box Models for Early
  Alzheimer's Disease Classification Evaluated for Multiple Datasets

Machine Learning Workflow to Explain Black-box Models for Early Alzheimer's Disease Classification Evaluated for Multiple Datasets

12 May 2022
Louise Bloch
Christoph M. Friedrich
ArXivPDFHTML

Papers citing "Machine Learning Workflow to Explain Black-box Models for Early Alzheimer's Disease Classification Evaluated for Multiple Datasets"

10 / 10 papers shown
Title
dalex: Responsible Machine Learning with Interactive Explainability and
  Fairness in Python
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python
Hubert Baniecki
Wojciech Kretowicz
Piotr Piątyszek
J. Wiśniewski
P. Biecek
FaML
56
97
0
28 Dec 2020
Problems with Shapley-value-based explanations as feature importance
  measures
Problems with Shapley-value-based explanations as feature importance measures
Indra Elizabeth Kumar
Suresh Venkatasubramanian
C. Scheidegger
Sorelle A. Friedler
TDI
FAtt
79
365
0
25 Feb 2020
Convolutional Neural Networks for Classification of Alzheimer's Disease:
  Overview and Reproducible Evaluation
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
Junhao Wen
Elina Thibeau-Sutre
Mauricio Diaz-Melo
J. Samper-González
A. Routier
Simona Bottani
Didier Dormont
S. Durrleman
Ninon Burgos
O. Colliot
64
520
0
16 Apr 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really
  Learn
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Sebastian Lapuschkin
S. Wäldchen
Alexander Binder
G. Montavon
Wojciech Samek
K. Müller
100
1,015
0
26 Feb 2019
Visual Explanations From Deep 3D Convolutional Neural Networks for
  Alzheimer's Disease Classification
Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification
Chengliang Yang
Anand Rangarajan
Sanjay Ranka
FAtt
40
142
0
07 Mar 2018
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
1.2K
16,990
0
16 Feb 2016
Striving for Simplicity: The All Convolutional Net
Striving for Simplicity: The All Convolutional Net
Jost Tobias Springenberg
Alexey Dosovitskiy
Thomas Brox
Martin Riedmiller
FAtt
248
4,672
0
21 Dec 2014
Deep Inside Convolutional Networks: Visualising Image Classification
  Models and Saliency Maps
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan
Andrea Vedaldi
Andrew Zisserman
FAtt
312
7,295
0
20 Dec 2013
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
595
15,882
0
12 Nov 2013
Practical Bayesian Optimization of Machine Learning Algorithms
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek
Hugo Larochelle
Ryan P. Adams
356
7,942
0
13 Jun 2012
1