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20212025
most citedSuperficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions

58 citations · 117 across the 9 of their papers we have counts for

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cs.CV2025

ScSAM: Debiasing Morphology and Distributional Variability in Subcellular Semantic Segmentation

Bo Fang, Jianan Fan, Dongnan Liu +4

The significant morphological and distributional variability among subcellular components poses a long-standing challenge for learning-based organelle segmentation models, signific…

cs.CV2025

TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of View

Yuqian Chen, Leo Zekelman, Yui Lo +7

Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical s…

cs.CV2022

White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning

Yuqian Chen, Fan Zhang, Chaoyi Zhang +9

White matter tract microstructure has been shown to influence neuropsychological scores of cognitive performance. However, prediction of these scores from white matter tract data h…

cs.CV2022

Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling

Canran Li, Dongnan Liu, Haoran Li +4

Unsupervised domain adaptation (UDA) methods have been broadly utilized to improve the models' adaptation ability in general computer vision. However, different from the natural im…

cs.CV2022★ 34 cited

Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation

Yuqian Chen, Chaoyi Zhang, Tengfei Xue +6

White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination…