72 citations · 72 across the 6 of their papers we have counts for
6 papers
Native Intelligence Emerges from Large-Scale Clinical Practice: A Retinal Foundation Model with Deployment Efficiency
Jia Guo, Jiawei Du, Shengzhu Yang +21
Current retinal foundation models remain constrained by curated research datasets that lack authentic clinical context, and require extensive task-specific optimization for each ap…
CLIPin: A Non-contrastive Plug-in to CLIP for Multimodal Semantic Alignment
Shengzhu Yang, Jiawei Du, Shuai Lu +3
Large-scale natural image-text datasets, especially those automatically collected from the web, often suffer from loose semantic alignment due to weak supervision, while medical da…
RetSTA: An LLM-Based Approach for Standardizing Clinical Fundus Image Reports
Jiushen Cai, Weihang Zhang, Hanruo Liu +2
Standardization of clinical reports is crucial for improving the quality of healthcare and facilitating data integration. The lack of unified standards, including format, terminolo…
ViLReF: An Expert Knowledge Enabled Vision-Language Retinal Foundation Model
Shengzhu Yang, Jiawei Du, Jia Guo +4
Subtle semantic differences in retinal image and text data present great challenges for pre-training visual-language models. Moreover, false negative samples, i.e., image-text pair…
RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports
Jiawei Du, Jia Guo, Weihang Zhang +4
The Vision-Language Foundation model is increasingly investigated in the fields of computer vision and natural language processing, yet its exploration in ophthalmology and broader…
VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence
Jianing Qiu, Jian Wu, Hao Wei +39
We present VisionFM, a foundation model pre-trained with 3.4 million ophthalmic images from 560,457 individuals, covering a broad range of ophthalmic diseases, modalities, imaging…