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Found 6 papers, 2 papers with code
Frequency Disentangled Learning for Segmentation of Midbrain Structures from Quantitative Susceptibility Mapping Data
One may hypothesize that such property can be leveraged for better training of deep learning models.
Fourier Disentangled Multimodal Prior Knowledge Fusion for Red Nucleus Segmentation in Brain MRI
In this paper, we propose a new model that integrates prior knowledge from different contrasts for red nucleus segmentation.
Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse
In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse.
Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort
Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader$^{TM}$); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists.
Visualization approach to assess the robustness of neural networks for medical image classification
We demonstrated that the areas identified by the CNN were consistent with what is known of Alzheimer's disease and that the visualization approach extract coherent longitudinal patterns.
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics.
