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Found 4 papers, 3 papers with code
Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs
Neuroimaging data, e. g. obtained from magnetic resonance imaging (MRI), is comparably homogeneous due to (1) the uniform structure of the brain and (2) additional efforts to spatially normalize the data to a standard template using linear and non-linear transformations.
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of...
Visualizing evidence for Alzheimer's disease in deep neural networks trained on structural MRI data
In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data.
2D Human Pose Estimation
Quantitative Methods
Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease
In summary, we show that applying different visualization methods is important to understand the decisions of a CNN, a step that is crucial to increase clinical impact and trust in computer-based decision support systems.
