14 citations · 22 across the 7 of their papers we have counts for
7 papers
3D-U-SAM Network For Few-shot Tooth Segmentation in CBCT Images
Yifu Zhang, Zuozhu Liu, Yang Feng +1
Accurate representation of tooth position is extremely important in treatment. 3D dental image segmentation is a widely used method, however labelled 3D dental datasets are a scarc…
On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning
Jianhong Bai, Zuozhu Liu, Hualiang Wang +4
Though Self-supervised learning (SSL) has been widely studied as a promising technique for representation learning, it doesn't generalize well on long-tailed datasets due to the ma…
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis
Jiaxiang Liu, Tianxiang Hu, Yan Zhang +3
Zero-shot medical image classification is a critical process in real-world scenarios where we have limited access to all possible diseases or large-scale annotated data. It involve…
ToothSegNet: Image Degradation meets Tooth Segmentation in CBCT Images
Jiaxiang Liu, Tianxiang Hu, Yang Feng +2
In computer-assisted orthodontics, three-dimensional tooth models are required for many medical treatments. Tooth segmentation from cone-beam computed tomography (CBCT) images is a…
OrthoGAN:High-Precision Image Generation for Teeth Orthodontic Visualization
Feihong Shen, JIngjing Liu, Jianwen Lou +6
Patients take care of what their teeth will be like after the orthodontics. Orthodontists usually describe the expectation movement based on the original smile images, which is unc…
TFormer: 3D Tooth Segmentation in Mesh Scans with Geometry Guided Transformer
Huimin Xiong, Kunle Li, Kaiyuan Tan +4
Optical Intra-oral Scanners (IOS) are widely used in digital dentistry, providing 3-Dimensional (3D) and high-resolution geometrical information of dental crowns and the gingiva. A…
AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications
Jin Hao, Jiaxiang Liu, Jin Li +17
A critical step in virtual dental treatment planning is to accurately delineate all tooth-bone structures from CBCT with high fidelity and accurate anatomical information. Previous…