Interpreting deep learning models for glioma survival classification using visualization and textual explanations

Interpreting deep learning models for glioma survival classification using visualization and textual explanations

Abstract Background Saliency-based algorithms are able to explain the relationship between input image pixels and deep-learning model predictions. However, it may be difficult to assess the clinical value of the most important image features and the model predictions derived from the raw saliency ma...

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Bibliographic Details
Main Authors: Michael Osadebey, Qinghui Liu, Elies Fuster-Garcia, Kyrre E. Emblem
Format: Article
Language:English
Published: BMC 2023-10-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Glioblastoma
Magnetic Resonance Imaging (MR1)
3D Gradient Weighted Class Activation Mapping (3D-Grad-CAM)
Deep learning
Convolutional Neural Network (CNN)
Online Access:https://doi.org/10.1186/s12911-023-02320-2

Internet

https://doi.org/10.1186/s12911-023-02320-2

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