14 citations · 34 across the 10 of their papers we have counts for
12 papers
U-Bench: A Comprehensive Understanding of U-Net through 100-Variant Benchmarking
Fenghe Tang, Chengqi Dong, Wenxin Ma +7
Over the past decade, U-Net has been the dominant architecture in medical image segmentation, leading to the development of thousands of U-shaped variants. Despite its widespread a…
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…
HySparK: Hybrid Sparse Masking for Large Scale Medical Image Pre-Training
Fenghe Tang, Ronghao Xu, Qingsong Yao +5
The generative self-supervised learning strategy exhibits remarkable learning representational capabilities. However, there is limited attention to end-to-end pre-training methods…
Slide-SAM: Medical SAM Meets Sliding Window
Quan Quan, Fenghe Tang, Zikang Xu +2
The Segment Anything Model (SAM) has achieved a notable success in two-dimensional image segmentation in natural images. However, the substantial gap between medical and natural im…
UOD: Universal One-shot Detection of Anatomical Landmarks
Heqin Zhu, Quan Quan, Qingsong Yao +2
One-shot medical landmark detection gains much attention and achieves great success for its label-efficient training process. However, existing one-shot learning methods are highly…
Unsupervised augmentation optimization for few-shot medical image segmentation
Quan Quan, Shang Zhao, Qingsong Yao +2
The augmentation parameters matter to few-shot semantic segmentation since they directly affect the training outcome by feeding the networks with varying perturbated samples. Howev…