Yufang Huang

Papers

Total Citations

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About

Yufang Huang’s research centers on medical image analysis, with a particular focus on deformable image registration—a critical task for medical robotics, motion analysis, intra-operative tracking, and segmentation. Her most cited work, “Test-Time Training for Deformable Multi-Scale Image Registration” (2021, 18 citations), introduces a novel framework that optimizes registration at test time, improving accuracy and adaptability over traditional methods like ANTs and NiftyReg. This contribution addresses key limitations in conventional approaches, which often struggle with varying image pairs. Huang’s approach leverages multi-scale processing and test-time training to enhance robustness, making it highly relevant for real-time clinical applications. With a growing citation impact, her work is recognized for bridging the gap between algorithmic efficiency and practical deployment in medical robotics. Huang’s research not only advances foundational registration techniques but also supports downstream tasks such as image segmentation and motion tracking, underscoring her role in pushing the boundaries of automated medical imaging.