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Found 6 papers, 3 papers with code
Synthesis and Perceptual Scaling of High Resolution Natural Images Using Stable Diffusion
Previous work on natural scenes has frequently focused on collections of discrete images with considerable physical differences from stimulus to stimulus.
Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease
Methods: We trained a CNN for the detection of AD in N=663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including N=1655 cases.
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
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 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.
The Same Analysis Approach: Practical protection against the pitfalls of novel neuroimaging analysis methods
We stress the importance of keeping the analysis method the same in main and test analyses, because only this way possible confounds and unexpected properties can be reliably detected and avoided.
Neurons and Cognition Applications
