6 papers
Enhancing Medical Visual Grounding via Knowledge-guided Spatial Prompts
Yifan Gao, Tao Zhou, Yi Zhou +3
Medical Visual Grounding (MVG) aims to identify diagnostically relevant phrases from free-text radiology reports and localize their corresponding regions in medical images, providi…
FusionFM: Fusing Eye-specific Foundational Models for Optimized Ophthalmic Diagnosis
Ke Zou, Jocelyn Hui Lin Goh, Yukun Zhou +11
Foundation models (FMs) have shown great promise in medical image analysis by improving generalization across diverse downstream tasks. In ophthalmology, several FMs have recently…
A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers
Meng Wang, Tian Lin, Qingshan Hou +37
Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings,…
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…
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…
Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection?
Samantha Min Er Yew, Xiaofeng Lei, Jocelyn Hui Lin Goh +26
Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications. However, its comparative performance with traditional d…