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Methodological Explainability Evaluation of an Interpretable Deep
  Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating
  Counterfactual Explanations and Layerwise Relevance Propagation: A
  Prospective In Silico Trial

Methodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial

7 August 2024
Xian Zhong
Zohaib Salahuddin
Yi Chen
Henry C. Woodruff
H. Long
Jianyun Peng
Nuwan Udawatte
Roberto Casale
Ayoub Mokhtari
Xiao′er Zhang
Jiayao Huang
Qingyu Wu
Li Tan
Lili Chen
Dongming Li
Xiaoyan Xie
Manxia Lin
Philippe Lambin
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Papers citing "Methodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial"

2 / 2 papers shown
Title
Using StyleGAN for Visual Interpretability of Deep Learning Models on
  Medical Images
Using StyleGAN for Visual Interpretability of Deep Learning Models on Medical Images
K. Schutte
O. Moindrot
P. Hérent
Jean-Baptiste Schiratti
S. Jégou
FAtt
MedIm
31
60
0
19 Jan 2021
Explaining the Black-box Smoothly- A Counterfactual Approach
Explaining the Black-box Smoothly- A Counterfactual Approach
Junyu Chen
Yong Du
Yufan He
W. Paul Segars
Ye Li
MedIm
FAtt
65
83
0
11 Jan 2021
1