12 citations · 20 across the 9 of their papers we have counts for
11 papers
RetiJev: Fast and Source-Grounded Ophthalmic Decision Models
Bo Liu, Lianyu Wang, Meng Wang +2
Ophthalmic clinical decision support requires fast, accurate, and privacy-preserving inference, yet autoregressive large language models (LLMs) incur substantial latency and comput…
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…
Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers
Meng Wang, Tian Lin, Qingshan Hou +69
Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings,…
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…
Say No to Freeloader: Protecting Intellectual Property of Your Deep Model
Lianyu Wang, Meng Wang, Huazhu Fu +1
Model intellectual property (IP) protection has attracted growing attention as science and technology advancements stem from human intellectual labor and computational expenses. En…
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…
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Meng Wang, Tian Lin, Lianyu Wang +21
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classific…
Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection
Lianyu Wang, Meng Wang, Daoqiang Zhang +1
As scientific and technological advancements result from human intellectual labor and computational costs, protecting model intellectual property (IP) has become increasingly impor…
Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging
Meng Wang, Lianyu Wang, Xinxing Xu +5
Deep learning models have shown promising performance in the field of diabetic retinopathy (DR) staging. However, collaboratively training a DR staging model across multiple instit…
Reliable Federated Disentangling Network for Non-IID Domain Feature
Meng Wang, Kai Yu, Chun-Mei Feng +6
Federated learning (FL), as an effective decentralized distributed learning approach, enables multiple institutions to jointly train a model without sharing their local data. Howev…
Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain
Lianyu Wang, Meng Wang, Daoqiang Zhang +1
Unsupervised domain adaptation (UDA) has attracted considerable attention, which transfers knowledge from a label-rich source domain to a related but unlabeled target domain. Reduc…