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Uncovering convolutional neural network decisions for diagnosing
  multiple sclerosis on conventional MRI using layer-wise relevance propagation

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

18 April 2019
Fabian Eitel
Emily Soehler
J. Bellmann-Strobl
A. Brandt
K. Ruprecht
René M. Giess
J. Kuchling
Susanna Asseyer
M. Weygandt
J. Haynes
M. Scheel
Friedemann Paul
K. Ritter
ArXivPDFHTML

Papers citing "Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation"

12 / 12 papers shown
Title
Pfungst and Clever Hans: Identifying the unintended cues in a widely used Alzheimer's disease MRI dataset using explainable deep learning
Pfungst and Clever Hans: Identifying the unintended cues in a widely used Alzheimer's disease MRI dataset using explainable deep learning
C. Tinauer
Maximilian Sackl
Rudolf Stollberger
Stefan Ropele
C. Langkammer
AAML
40
0
0
27 Jan 2025
GAMER-MRIL identifies Disability-Related Brain Changes in Multiple
  Sclerosis
GAMER-MRIL identifies Disability-Related Brain Changes in Multiple Sclerosis
Po-Jui Lu
Benjamin Odry
M. Barakovic
Matthias Weigel
Robin Sandkühler
...
Mario Ocampo Pineda
J. Kuhle
L. Kappos
Philippe C. Cattin
C. Granziera
30
0
0
15 Aug 2023
Interpretability of Machine Learning Methods Applied to Neuroimaging
Interpretability of Machine Learning Methods Applied to Neuroimaging
Elina Thibeau-Sutre
S. Collin
Ninon Burgos
O. Colliot
16
4
0
14 Apr 2022
Brain Structural Saliency Over The Ages
Brain Structural Saliency Over The Ages
Daniel Taylor
Jonathan Shock
Deshendran Moodley
J. Ipser
M. Treder
FAtt
9
1
0
12 Jan 2022
Evaluating saliency methods on artificial data with different background
  types
Evaluating saliency methods on artificial data with different background types
Céline Budding
Fabian Eitel
K. Ritter
Stefan Haufe
XAI
FAtt
MedIm
18
5
0
09 Dec 2021
Transparency of Deep Neural Networks for Medical Image Analysis: A
  Review of Interpretability Methods
Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods
Zohaib Salahuddin
Henry C. Woodruff
A. Chatterjee
Philippe Lambin
15
301
0
01 Nov 2021
Applications of Deep Learning Techniques for Automated Multiple
  Sclerosis Detection Using Magnetic Resonance Imaging: A Review
Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review
A. Shoeibi
Marjane Khodatars
M. Jafari
Parisa Moridian
Mitra Rezaei
...
Juan M Gorriz
Jónathan Heras
M. Panahiazar
S. Nahavandi
U. Acharya
24
125
0
11 May 2021
Interpretable Deep Learning for the Remote Characterisation of
  Ambulation in Multiple Sclerosis using Smartphones
Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones
Andrew P. Creagh
F. Lipsmeier
M. Lindemann
M. D. Vos
21
17
0
16 Mar 2021
When Explanations Lie: Why Many Modified BP Attributions Fail
When Explanations Lie: Why Many Modified BP Attributions Fail
Leon Sixt
Maximilian Granz
Tim Landgraf
BDL
FAtt
XAI
11
132
0
20 Dec 2019
Towards Explainable Artificial Intelligence
Towards Explainable Artificial Intelligence
Wojciech Samek
K. Müller
XAI
21
435
0
26 Sep 2019
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
234
2,235
0
24 Jun 2017
A Survey on Deep Learning in Medical Image Analysis
A Survey on Deep Learning in Medical Image Analysis
G. Litjens
Thijs Kooi
B. Bejnordi
A. Setio
F. Ciompi
Mohsen Ghafoorian
Jeroen van der Laak
Bram van Ginneken
C. I. Sánchez
OOD
283
10,613
0
19 Feb 2017
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