59 citations · 93 across the 12 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…
PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancement
Zhen Huang, Han Li, Shitong Shao +5
The pelvis, the lower part of the trunk, supports and balances the trunk. Landmark detection from a pelvic X-ray (PXR) facilitates downstream analysis and computer-assisted diagnos…
DFTR: Depth-supervised Fusion Transformer for Salient Object Detection
Heqin Zhu, Xu Sun, Yuexiang Li +3
Automated salient object detection (SOD) plays an increasingly crucial role in many computer vision applications. By reformulating the depth information as supervision rather than…
DATR: Domain-adaptive transformer for multi-domain landmark detection
Heqin Zhu, Qingsong Yao, S. Kevin Zhou
Accurate anatomical landmark detection plays an increasingly vital role in medical image analysis. Although existing methods achieve satisfying performance, they are mostly based o…
You Only Learn Once: Universal Anatomical Landmark Detection
Heqin Zhu, Qingsong Yao, Li Xiao +1
Detecting anatomical landmarks in medical images plays an essential role in understanding the anatomy and planning automated processing. In recent years, a variety of deep neural n…
Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
Pengbo Liu, Hu Han, Yuanqi Du +9
Purpose: Pelvic bone segmentation in CT has always been an essential step in clinical diagnosis and surgery planning of pelvic bone diseases. Existing methods for pelvic bone segme…
ADSegNet: Anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation
Yuanyuan Lyu, Haofu Liao, Heqin Zhu +1
Spinal surgery planning necessitates automatic segmentation of vertebrae in cone-beam computed tomography (CBCT), an intraoperative imaging modality that is widely used in interven…