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Found 6 papers, 3 papers with code
Slicer Networks
In medical imaging, scans often reveal objects with varied contrasts but consistent internal intensities or textures.
RimSet: Quantitatively Identifying and Characterizing Chronic Active Multiple Sclerosis Lesion on Quantitative Susceptibility Maps
We validated RimSet using simulated QSM images and an in vivo dataset of 172 MS subjects with 177 rim+ and 3986 rim-lesions.
Spatially Covariant Image Registration with Text Prompts
Medical images are often characterized by their structured anatomical representations and spatially inhomogeneous contrasts.
Ranked #2 on
Image Registration
on Unpaired-abdomen-CT
(using extra training data)
Multi-delay arterial spin-labeled perfusion estimation with biophysics simulation and deep learning
The trained network was further tested in a synthetic brain ASL image based on vasculature network extracted from magnetic resonance (MR) angiography.
DAGrid: Directed Accumulator Grid
Specifically, the network incorporating DAGrid has realized a 70. 8% reduction in network parameter size and a 96. 8% decrease in FLOPs, while concurrently improving the Dice score for skin lesion segmentation by 1. 0% compared to state-of-the-art transformers.
DeDA: Deep Directed Accumulator
Chronic active multiple sclerosis lesions, also termed as rim+ lesions, can be characterized by a hyperintense rim at the edge of the lesion on quantitative susceptibility maps.
