3 citations · 6 across the 13 of their papers we have counts for
5 papers · 1 filter
FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation
Qingshan Hou, Meng Wang, Peng Cao +4
Recent advancements in ophthalmology foundation models such as RetFound have demonstrated remarkable diagnostic capabilities but require massive datasets for effective pre-training…
Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Images
Yuanyuan Peng, Aidi Lin, Meng Wang +10
Inability to express the confidence level and detect unseen classes has limited the clinical implementation of artificial intelligence in the real-world. We developed a foundation…
Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Model
Meng Wang, Tian Lin, Aidi Lin +46
Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base. Here we introduce a knowledge-rich vision-language model (RetiZero…
Reliable Source Approximation: Source-Free Unsupervised Domain Adaptation for Vestibular Schwannoma MRI Segmentation
Hongye Zeng, Ke Zou, Zhihao Chen +2
Source-Free Unsupervised Domain Adaptation (SFUDA) has recently become a focus in the medical image domain adaptation, as it only utilizes the source model and does not require ann…
Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty
Ke Zou, Yidi Chen, Ling Huang +6
Medical image segmentation is critical for disease diagnosis and treatment assessment. However, concerns regarding the reliability of segmentation regions persist among clinicians,…