4 citations · 11 across the 10 of their papers we have counts for
7 papers · 1 filter
SSA3D: Text-Conditioned Assisted Self-Supervised Framework for Automatic Dental Abutment Design
Mianjie Zheng, Xinquan Yang, Along He +6
Abutment design is a critical step in dental implant restoration. However, manual design involves tedious measurement and fitting, and research on automating this process with AI i…
GEMeX-RMCoT: An Enhanced Med-VQA Dataset for Region-Aware Multimodal Chain-of-Thought Reasoning
Bo Liu, Xiangyu Zhao, Along He +3
Medical visual question answering aims to support clinical decision-making by enabling models to answer natural language questions based on medical images. While recent advances in…
Cross-Frequency Collaborative Training Network and Dataset for Semi-supervised First Molar Root Canal Segmentation
Zhenhuan Zhou, Yuchen Zhang, Along He +3
Root canal (RC) treatment is a highly delicate and technically complex procedure in clinical practice, heavily influenced by the clinicians' experience and subjective judgment. Dee…
FRCNet Frequency and Region Consistency for Semi-supervised Medical Image Segmentation
Along He, Tao Li, Yanlin Wu +2
Limited labeled data hinder the application of deep learning in medical domain. In clinical practice, there are sufficient unlabeled data that are not effectively used, and semi-su…
ResEnsemble-DDPM: Residual Denoising Diffusion Probabilistic Models for Ensemble Learning
Shi Zhenning, Dong Changsheng, Xie Xueshuo +3
Nowadays, denoising diffusion probabilistic models have been adapted for many image segmentation tasks. However, existing end-to-end models have already demonstrated remarkable cap…
Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise
Zhenning Shi, Haoshuai Zheng, Chen Xu +6
Recently, research on denoising diffusion models has expanded its application to the field of image restoration. Traditional diffusion-based image restoration methods utilize degra…
DVPT: Dynamic Visual Prompt Tuning of Large Pre-trained Models for Medical Image Analysis
Along He, Kai Wang, Zhihong Wang +2
Limited labeled data makes it hard to train models from scratch in medical domain, and an important paradigm is pre-training and then fine-tuning. Large pre-trained models contain…