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Found 4 papers, 2 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.
Covid-19 -- A simple statistical model for predicting ICU load in early phases of the disease
Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated.
Populations and Evolution Applications
Harnessing spatial MRI normalization: patch individual filter layers for CNNs
Neuroimaging studies based on magnetic resonance imaging (MRI) typically employ rigorous forms of preprocessing.
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...
