61 citations · 294 across the 30 of their papers we have counts for
18 papers · 1 filter
FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation
Jianghao Wu, Dong Guo, Guotai Wang +4
Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised D…
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation
Zhongzhen Huang, Linda Wei, Shaoting Zhang +1
Combining images from multi-modalities is beneficial to explore various information in computer vision, especially in the medical domain. As an essential part of clinical diagnosis…
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine
Xiaosong Wang, Xiaofan Zhang, Guotai Wang +17
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and…
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…
UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation
Jianghao Wu, Guotai Wang, Ran Gu +6
Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are us…
Scribble-based 3D Multiple Abdominal Organ Segmentation via Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency
Meng Han, Xiangde Luo, Wenjun Liao +3
Multi-organ segmentation in abdominal Computed Tomography (CT) images is of great importance for diagnosis of abdominal lesions and subsequent treatment planning. Though deep learn…