Deep Radiomic Analysis Based on Modeling Information Flow in Convolutional Neural Networks

Deep Radiomic Analysis Based on Modeling Information Flow in Convolutional Neural Networks

This paper proposes a novel image feature set based on a principled information theoretic analysis of the convolutional neural network (CNN). The output of convolutional filters is modeled as a random variable conditioned on the object class and network filter bank. The conditional entropy (CENT) of...

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Bibliographic Details
Main Authors: Ahmad Chaddad, Matthew Toews, Christian Desrosiers, Tamim Niazi
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Entropy
information flow
deep learning
radiomics
Online Access:https://ieeexplore.ieee.org/document/8767919/

Internet

https://ieeexplore.ieee.org/document/8767919/

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