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Interpretable Deep Models for Cardiac Resynchronisation Therapy Response
  Prediction

Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction

24 June 2020
Esther Puyol-Antón
Cheng Chen
J. Clough
B. Ruijsink
B. Sidhu
J. Gould
B. Porter
M. Elliott
Vishal S. Mehta
Daniel Rueckert
C. Rinaldi
A. King
ArXivPDFHTML

Papers citing "Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction"

6 / 6 papers shown
Title
Attri-VAE: attribute-based interpretable representations of medical
  images with variational autoencoders
Attri-VAE: attribute-based interpretable representations of medical images with variational autoencoders
Irem Cetin
Maialen Stephens
Oscar Camara
M. A. G. Ballester
DRL
38
39
0
20 Mar 2022
Echocardiography Segmentation with Enforced Temporal Consistency
Echocardiography Segmentation with Enforced Temporal Consistency
Nathan Painchaud
Nicolas Duchateau
Olivier Bernard
Pierre-Marc Jodoin
24
49
0
03 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
Uncertainty-Aware Training for Cardiac Resynchronisation Therapy
  Response Prediction
Uncertainty-Aware Training for Cardiac Resynchronisation Therapy Response Prediction
Tareen Dawood
C. L. P. Chen
R. Andlauer
B. Sidhu
B. Ruijsink
...
Vishal S. Mehta
C. Rinaldi
Esther Puyol-Antón
Reza Razavi
A. King
15
2
0
22 Sep 2021
ProtoPShare: Prototype Sharing for Interpretable Image Classification
  and Similarity Discovery
ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery
Dawid Rymarczyk
Lukasz Struski
Jacek Tabor
Bartosz Zieliñski
19
111
0
29 Nov 2020
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
251
3,684
0
28 Feb 2017
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