AIM for Health Lab · Ophthalmology

Seeing Health Through the Retina

We develop intelligent systems that bridge AI and ophthalmic medicine, leveraging foundation models, oculomics, and multi-agent architectures to advance retinal image understanding for both ocular and systemic disease.

Explore Our Research About Lab
Publications

Research

Foundation arXiv 2026
RetSAM: A General Model for Retinal Segmentation and Quantification
Zhonghua Wang, Lie Ju, Sijia Li, Wei Feng, Sijin Zhou, Ming Hu, Jianhao Xiong, Xiaoying Tang, Yifan Peng, Mingquan Lin, Yaodong Ding, Yong Zeng, Wenbin Wei, Li Dong, Zongyuan Ge
Foundation IEEE TMI 2024
Hierarchical Knowledge Guided Learning for Real-World Retinal Disease Recognition
Lie Ju, Zhen Yu, Lin Wang, Xin Zhao, Xin Wang, Paul Bonnington, Zongyuan Ge
Foundation MICCAI 2021
Relational Subsets Knowledge Distillation for Long-Tailed Retinal Diseases Recognition
Lie Ju, Xin Wang, Lin Wang, Tongliang Liu, Xin Zhao, Tom Drummond, Dwarikanath Mahapatra, Zongyuan Ge
Foundation MICCAI 2019
Retinal Abnormalities Recognition Using Regional Multitask Learning
Xin Wang, Lie Ju, Xin Zhao, Zongyuan Ge
Oculomics Prog. Retin. Eye Res. 2025
Oculomics: Current Concepts and Evidence
Zhuoting Zhu, Yueye Wang, Ziyi Qi, Wenyi Hu, Xiayin Zhang, Siegfried K. Wagner, ..., Zongyuan Ge, ..., Pearse A. Keane, ..., Tien Yin Wong
Oculomics Journal of Big Data 2023
Retinal Photograph-Based Deep Learning System for Detection of Hyperthyroidism: A Multicenter, Diagnostic Study
Li Dong*, Lie Ju*, Shiqi Hui*, Lihua Luo*, ..., Jost B. Jonas, Zongyuan Ge, Wenbin Wei, Dongmei Li
Oculomics Science Bulletin 2022
Deep Learning Algorithm Using Fundus Photographs for 10-Year Risk Assessment of Ischemic Cardiovascular Diseases in China
Yanjun Ma, Jianhao Xiong, Yidan Zhu, Zongyuan Ge, Rong Hua, Meng Fu, Chenglong Li, et al.
Oculomics Eye 2023
Artificial Intelligence to Distinguish Retinal Vein Occlusion Patients Using Color Fundus Photographs
Xiang Ren*, Wei Feng*, Ting Wang, Bin Wang, Lie Ju, Yuzhong Chen, Lanqing He, ..., Zongyuan Ge, Ming Zhang
Agent Under Review 2026
OphAgent: A Multi-Agent Ophthalmology AI System Validated Across 311 Ophthalmologists From 86 Centers in 22 Countries
Lie Ju et al.
System Lancet Digital Health 2021
Application of Comprehensive Artificial Intelligence Retinal Expert (CARE) System: A National Real-World Evidence Study
Duoru Lin, Jianhao Xiong, Congxin Liu, Lanqin Zhao, Zhongwen Li, Shanshan Yu, Xiaohang Wu, et al.
Other IEEE JBHI 2021
Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-Task Learning
Lie Ju, Xin Wang, Xin Zhao, Paul Bonnington, Tom Drummond, Zongyuan Ge
Other AAAI 2022
Improving Medical Image Classification with Label Noise Using Dual-Uncertainty Estimation
Lie Ju, Xin Wang, Lin Wang, Dwarikanath Mahapatra, Xin Zhao, Quan Zhou, Tom Drummond, Zongyuan Ge
Other IOVS 2024
Explore Vision-Language Model with Hierarchical Information for Multiple Retinal Disease Recognition
Lie Ju et al.
People

Team

ZG
Group Leader
Founding Director, AIM for Health Lab
ZW
Zhonghua Wang
PhD Student
SZ
Sijin Zhou
PhD Student
SL
Sijia Li
PhD Student
FT
Feilong Tang
PhD Student

Collaborators

LJ
Postdoctoral Researcher
UCL & Moorfields Eye Hospital
PK
Professor
UCL & Moorfields Eye Hospital
About

About

AIM for Ophthalmology is a research group within the AIM for Health Lab at Monash University, with active collaboration at UCL Institute of Ophthalmology and the NIHR Biomedical Research Centre at Moorfields Eye Hospital. Our research focuses on building AI systems for ophthalmic imaging, spanning three interconnected directions: foundation models for robust retinal disease recognition, oculomics that leverages retinal imaging to predict systemic diseases, and multi-agent architectures for clinical ophthalmology decision support.

The AIM for Health Lab (Augmented Intelligence and Multimodal Analytics for Health) is founded and directed by A/Prof. Zongyuan Ge. The lab spans expertise in health AI translation, privacy-preserving AI, federated learning, and multimodal data analysis, with deep connections to first-tier healthcare providers and industry partners. Research from the lab has been published in leading venues including Nature Medicine, Nature Nanotechnology, Science Advances, The Lancet Digital Health, and top AI conferences such as NeurIPS, CVPR, and MICCAI.

We are always looking for passionate PhD students, postdocs, and visiting scholars. Feel free to reach out via Zongyuan.Ge@monash.edu.

Monash University AIM for Health Lab