Search Results for author:
Found 5 papers, 3 papers with code
MONAI: An open-source framework for deep learning in healthcare
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
Optimal Transport Features for Morphometric Population Analysis
We demonstrate the proposed optimal transport feature extraction step in the context of a volumetric morphometric analysis of the OASIS-1 study for Alzheimer's disease.
Deep Decomposition for Stochastic Normal-Abnormal Transport
Extensive comparisons demonstrate that our model successfully distinguishes stroke lesions (abnormal) from normal brain regions, while reconstructing the underlying velocity and diffusion tensor fields.
Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach
Extracting the brain from images with strong pathologies, for example, the presence of a tumor or of a traumatic brain injury, is challenging.
Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach
Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and spatial transforms cannot account for common changes due to pathologies.
