1 citations · 1 across the 1 of their papers we have counts for
6 papers
Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detection
Qingsong Yao, Zhen Huang, Ao Wang +4
Medical landmark detection usually requires accurate expert annotations, which are laborious and difficult to scale across anatomical regions. In this work, we study an extreme ann…
H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical Imaging
Zhen Huang, Tao Tang, Ronghao Xu +6
3D landmark detection is a critical task in medical image analysis, and accurately detecting anatomical landmarks is essential for subsequent medical imaging tasks. However, mainst…
MedAtlas: Evaluating LLMs for Multi-Round, Multi-Task Medical Reasoning Across Diverse Imaging Modalities and Clinical Text
Ronghao Xu, Zhen Huang, Yangbo Wei +5
Artificial intelligence has demonstrated significant potential in clinical decision-making; however, developing models capable of adapting to diverse real-world scenarios and perfo…
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
Hongbo Ye, Fenghe Tang, Peiang Zhao +4
Achieving equity in healthcare accessibility requires lightweight yet high-performance solutions for medical image segmentation, particularly in resource-limited settings. Existing…
Landmarks Are Alike Yet Distinct: Harnessing Similarity and Individuality for One-Shot Medical Landmark Detection
Xu He, Zhen Huang, Qingsong Yao +2
Landmark detection plays a crucial role in medical imaging applications such as disease diagnosis, bone age estimation, and therapy planning. However, training models for detecting…
HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection
Xiaoqian Zhou, Zhen Huang, Heqin Zhu +2
Anatomical landmark detection (ALD) from a medical image is crucial for a wide array of clinical applications. While existing methods achieve quite some success in ALD, they often…