15 citations · 27 across the 4 of their papers we have counts for
7 papers · 1 filter
3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
Jieneng Chen, Jieru Mei, Xianhang Li +12
Medical image segmentation plays a crucial role in advancing healthcare systems for disease diagnosis and treatment planning. The u-shaped architecture, popularly known as U-Net, h…
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts
Shiyi Du, Xiaosong Wang, Yongyi Lu +5
Image synthesis approaches, e.g., generative adversarial networks, have been popular as a form of data augmentation in medical image analysis tasks. It is primarily beneficial to o…
Learning to In-paint: Domain Adaptive Shape Completion for 3D Organ Segmentation
Mingjin Chen, Yongkang He, Yongyi Lu +1
We aim at incorporating explicit shape information into current 3D organ segmentation models. Different from previous works, we formulate shape learning as an in-painting task, whi…
Data-Centric Diet: Effective Multi-center Dataset Pruning for Medical Image Segmentation
Yongkang He, Mingjin Chen, Zhijing Yang +1
This paper seeks to address the dense labeling problems where a significant fraction of the dataset can be pruned without sacrificing much accuracy. We observe that, on standard me…
Open-World Pose Transfer via Sequential Test-Time Adaption
Junyang Chen, Xiaoyu Xian, Zhijing Yang +5
Pose transfer aims to transfer a given person into a specified posture, has recently attracted considerable attention. A typical pose transfer framework usually employs representat…
Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation
Yuan Yao, Fengze Liu, Zongwei Zhou +4
Shape information is a strong and valuable prior in segmenting organs in medical images. However, most current deep learning based segmentation algorithms have not taken shape info…