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Resolving challenges in deep learning-based analyses of
  histopathological images using explanation methods

Resolving challenges in deep learning-based analyses of histopathological images using explanation methods

15 August 2019
Miriam Hagele
P. Seegerer
Sebastian Lapuschkin
M. Bockmayr
Wojciech Samek
Frederick Klauschen
K. Müller
Alexander Binder
ArXivPDFHTML

Papers citing "Resolving challenges in deep learning-based analyses of histopathological images using explanation methods"

22 / 22 papers shown
Title
Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks
Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks
Lorenz Linhardt
Klaus-Robert Muller
G. Montavon
AAML
29
7
0
12 Apr 2023
From slides (through tiles) to pixels: an explainability framework for
  weakly supervised models in pre-clinical pathology
From slides (through tiles) to pixels: an explainability framework for weakly supervised models in pre-clinical pathology
Marco Bertolini
Van-Khoa Le
Jake Pencharz
A. Poehlmann
Djork-Arné Clevert
Santiago D. Villalba
F. Montanari
36
0
0
03 Feb 2023
Disentangled Explanations of Neural Network Predictions by Finding
  Relevant Subspaces
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
Pattarawat Chormai
J. Herrmann
Klaus-Robert Muller
G. Montavon
FAtt
48
17
0
30 Dec 2022
Data Models for Dataset Drift Controls in Machine Learning With Optical
  Images
Data Models for Dataset Drift Controls in Machine Learning With Optical Images
Luis Oala
Marco Aversa
Gabriel Nobis
Kurt Willis
Yoan Neuenschwander
...
E. Pomarico
Wojciech Samek
Roderick Murray-Smith
Christoph Clausen
B. Sanguinetti
28
5
0
04 Nov 2022
Attention-based Interpretable Regression of Gene Expression in Histology
Attention-based Interpretable Regression of Gene Expression in Histology
Mara Graziani
Niccolo Marini
Nicolas Deutschmann
Nikita Janakarajan
Henning Muller
María Rodríguez Martínez
MedIm
46
5
0
29 Aug 2022
Explain to Not Forget: Defending Against Catastrophic Forgetting with
  XAI
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI
Sami Ede
Serop Baghdadlian
Leander Weber
A. Nguyen
Dario Zanca
Wojciech Samek
Sebastian Lapuschkin
CLL
27
6
0
04 May 2022
Detection of Degraded Acacia tree species using deep neural networks on
  uav drone imagery
Detection of Degraded Acacia tree species using deep neural networks on uav drone imagery
A. Osio
Hoàng-Ân Lê
Samson Ayugi
F. Onyango
P. Odwe
Sébastien Lefèvre
11
2
0
14 Apr 2022
From Modern CNNs to Vision Transformers: Assessing the Performance,
  Robustness, and Classification Strategies of Deep Learning Models in
  Histopathology
From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology
Maximilian Springenberg
A. Frommholz
M. Wenzel
Eva Weicken
Jackie Ma
Nils Strodthoff
MedIm
30
42
0
11 Apr 2022
Multi-Attention Multiple Instance Learning
Multi-Attention Multiple Instance Learning
A. Konstantinov
Lev V. Utkin
28
13
0
11 Dec 2021
Explaining Bayesian Neural Networks
Explaining Bayesian Neural Networks
Kirill Bykov
Marina M.-C. Höhne
Adelaida Creosteanu
Klaus-Robert Muller
Frederick Klauschen
Shinichi Nakajima
Marius Kloft
BDL
AAML
34
25
0
23 Aug 2021
Explainable artificial intelligence (XAI) in deep learning-based medical
  image analysis
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
Bas H. M. van der Velden
Hugo J. Kuijf
K. Gilhuijs
M. Viergever
XAI
35
638
0
22 Jul 2021
On the Robustness of Pretraining and Self-Supervision for a Deep
  Learning-based Analysis of Diabetic Retinopathy
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy
Vignesh Srinivasan
Nils Strodthoff
Jackie Ma
Alexander Binder
Klaus-Robert Muller
Wojciech Samek
OOD
20
6
0
25 Jun 2021
GANterfactual - Counterfactual Explanations for Medical Non-Experts
  using Generative Adversarial Learning
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning
Silvan Mertes
Tobias Huber
Katharina Weitz
Alexander Heimerl
Elisabeth André
GAN
AAML
MedIm
34
69
0
22 Dec 2020
Towards Robust Explanations for Deep Neural Networks
Towards Robust Explanations for Deep Neural Networks
Ann-Kathrin Dombrowski
Christopher J. Anders
K. Müller
Pan Kessel
FAtt
24
63
0
18 Dec 2020
Quantifying Explainers of Graph Neural Networks in Computational
  Pathology
Quantifying Explainers of Graph Neural Networks in Computational Pathology
Guillaume Jaume
Pushpak Pati
Behzad Bozorgtabar
Antonio Foncubierta-Rodríguez
Florinda Feroce
A. Anniciello
T. Rau
Jean-Philippe Thiran
M. Gabrani
O. Goksel
FAtt
26
76
0
25 Nov 2020
It's All in the Name: A Character Based Approach To Infer Religion
It's All in the Name: A Character Based Approach To Infer Religion
Rochana Chaturvedi
Sugat Chaturvedi
24
23
0
27 Oct 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining
  Neural Networks
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks
Kirill Bykov
Marina M.-C. Höhne
Klaus-Robert Muller
Shinichi Nakajima
Marius Kloft
UQCV
FAtt
27
31
0
16 Jun 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and
  Applications
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Wojciech Samek
G. Montavon
Sebastian Lapuschkin
Christopher J. Anders
K. Müller
XAI
44
82
0
17 Mar 2020
Towards Best Practice in Explaining Neural Network Decisions with LRP
Towards Best Practice in Explaining Neural Network Decisions with LRP
M. Kohlbrenner
Alexander Bauer
Shinichi Nakajima
Alexander Binder
Wojciech Samek
Sebastian Lapuschkin
22
148
0
22 Oct 2019
Deep Weakly-Supervised Learning Methods for Classification and
  Localization in Histology Images: A Survey
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey
Jérôme Rony
Soufiane Belharbi
Jose Dolz
Ismail Ben Ayed
Luke McCaffrey
Eric Granger
25
70
0
08 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
2,238
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
310
10,621
0
19 Feb 2017
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