6 papers
Towards Clinically Interpretable Ophthalmic VQA via Spatially-Grounded Lesion Evidence
Xingyue Wang, Bo Liu, Meng Wang +4
Visual Question Answering (VQA) holds great promise for clinical support, particularly in ophthalmology, where retinal fundus photography is essential for diagnosis. However, ophth…
Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge
Shuai Lu, Meng Wang, Jia Guo +6
Large Vision Language Models (LVLMs) show immense potential for automated ophthalmic diagnosis. However, their clinical deployment is severely hindered by lacking domain-specific k…
Authorize-on-Demand: Dynamic Authorization with Legality-Aware Intellectual Property Protection for VLMs
Lianyu Wang, Meng Wang, Huazhu Fu +1
The rapid adoption of vision-language models (VLMs) has heightened the demand for robust intellectual property (IP) protection of these high-value pretrained models. Effective IP p…
Uncertainty-aware Medical Diagnostic Phrase Identification and Grounding
Ke Zou, Yang Bai, Bo Liu +9
Medical phrase grounding is crucial for identifying relevant regions in medical images based on phrase queries, facilitating accurate image analysis and diagnosis. However, current…
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…
Vision-Language Model IP Protection via Prompt-based Learning
Lianyu Wang, Meng Wang, Huazhu Fu +1
Vision-language models (VLMs) like CLIP (Contrastive Language-Image Pre-Training) have seen remarkable success in visual recognition, highlighting the increasing need to safeguard…