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20232026
most citedGS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation

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cs.CV2026

DINO-3DRA: Leveraging 2D Foundation Model Semantics for 3D Cerebral Aneurysm Segmentation

Jiayang Lu, Fengming Lin, Alejandro F. Frangi +1

Accurate aneurysm segmentation in 3D rotational angiography (3DRA) is hindered by extreme class imbalance, morphological similarity to vessels, and absent large-scale 3D pretrainin…

cs.CV2026

HeartVolMesh: Cardiac Volumetric Mesh Reconstruction via Covariance-Guided Graph Deformation

Fengming Lin, Arezoo Zakeri, Haoran Dou +4

Accurate patient-specific tetrahedral cardiac meshes are essential for in-silico trials, yet common segmentation-then-modelling pipelines can blur thin-wall anatomy and offer limit…

cs.CV2026

Conditional Latent Diffusion Model with Fourier-based Motion Modelling for Virtual Population Synthesis

Shaokun Lan, Haoran Dou, Jinghan Huang +5

In-silico trials of medical devices require the generation of virtual populations of anatomies. In cardiovascular applications, virtual anatomy is typically represented as a 3D+t m…

cs.CV2025

From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction

Fengming Lin, Arezoo Zakeri, Yidan Xue +7

Deep learning-based medical image-to-mesh reconstruction has rapidly evolved, enabling the transformation of medical imaging data into three-dimensional mesh models that are critic…

cs.CV20241 cited

GS-EMA: Integrating Gradient Surgery Exponential Moving Average with Boundary-Aware Contrastive Learning for Enhanced Domain Generalization in Aneurysm Segmentation

Fengming Lin, Yan Xia, Michael MacRaild +6

The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning. Confronted with significant domain shifts and class imbalance in 3D Rotat…

cs.CV2024

Unsupervised Domain Adaptation for Brain Vessel Segmentation through Transwarp Contrastive Learning

Fengming Lin, Yan Xia, Michael MacRaild +6

Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models. Since cros…