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Found 4 papers, 2 papers with code
GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation
The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning.
Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning
Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models.
Learned Local Attention Maps for Synthesising Vessel Segmentations
We present an encoder-decoder model for synthesising segmentations of the main cerebral arteries in the circle of Willis (CoW) from only T2 MRI.
Shape-guided Conditional Latent Diffusion Models for Synthesising Brain Vasculature
The Circle of Willis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain.
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