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Found 6 papers, 3 papers with code
Diffusion Models for Contrast Harmonization of Magnetic Resonance Images
We map images from the source contrast to the target contrast for both directions, from 3 T to 1. 5 T and from 1. 5 T to 3 T. As we only want to change the contrast, not the anatomical information, our method uses the original image to guide the image-to-image translation process by adding structural information.
Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis
This paper focuses on the uncertainty estimation for white matter lesions (WML) segmentation in magnetic resonance imaging (MRI).
Shifts 2.0: Extending The Dataset of Real Distributional Shifts
This creates a need to be able to assess how robustly ML models generalize as well as the quality of their uncertainty estimates.
Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues
Recently, advanced MS lesional imaging biomarkers such as cortical lesions (CL), the central vein sign (CVS), and paramagnetic rim lesions (PRL), visible in specialized magnetic resonance imaging (MRI) sequences, have shown higher specificity in differential diagnosis.
Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis
The limited availability of large image datasets, mainly due to data privacy and differences in acquisition protocols or hardware, is a significant issue in the development of accurate and generalizable machine learning methods in medicine.
Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis
In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients.
