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Found 5 papers, 2 papers with code
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation
One of the most common methods for analyzing CT and MRI images for diagnosis and follow-up treatment is liver segmentation.
An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological age-research.
Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks
For comparison, state-of-the-art models using preprocessed neuroimaging data are trained and validated on the same samples.
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies
Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions.
A Machine-learning framework for automatic reference-free quality assessment in MRI
Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest.
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