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Found 5 papers, 4 papers with code
Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings using Spine MRI data
Our findings highlight the potential of using advanced embedding techniques like DAEs to detect data quality issues and biases in medical imaging datasets.
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
A human-in-the-loop annotation workflow was employed, leveraging cross-modality transfer learning from an existing CT segmentation model to segment 40 anatomical structures.
SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation
Training on auto-generated annotations and evaluating on manually corrected test data from the GNC yielded global dice scores of 0. 900 for vertebrae, 0. 960 for intervertebral discs, and 0. 947 for the spinal canal.
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.
