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Document type:
Konferenzbeitrag 
Author(s):
Khakzar, A.; Albarqouni, S.; Navab, N. 
Title:
Learning Interpretable Features via Adversarially Robust Optimization 
Abstract:
Neural networks are proven to be remarkably successful for classification and diagnosis in medical applications. However, the ambiguity in the decision-making process and the interpretability of the learned features is a matter of concern. In this work, we propose a method for improving the feature interpretability of neural network classifiers. Initially, we propose a baseline convolutional neural network with state of the art performance in terms of accuracy and weakly supervised localization....    »
 
Keywords:
MICCAI,CAMP 
Book / Congress title:
International Conference on Medical Image Computing and Computer-Assisted Intervention 
Organization:
Springer 
Year:
2019 
Pages:
793--800