arXiv preprint
OPGAgent: An Agent for Auditable Dental Panoramic X-ray Interpretation
Zhaolin Yu, Litao Yang, Ben Babicka, Ming Hu, Jing Hao, Anthony Huang, James Huang, Yueming Jin, Jiasong Wu, Zongyuan Ge
AIM for Pathology Team
Decoding Health with Next-Generation AI.
We build intelligent foundation models and multimodal agents to bridge the gap between microscopic pathology and macroscopic medical imaging. From virtual cells to fully autonomous diagnostic systems.
Research
Cell Pathology
Developing large-scale foundation models for histopathology and computational pathology, enabling high-precision cancer grading and microenvironment analysis.
Dental AI
Building specialized vision-language models and autonomous agents for panoramic X-ray and CBCT analysis. Empowering next-generation dental diagnosis and treatment planning.
3D CCTA
Pushing the boundaries of 3D volume rendering and geometric deep learning for coronary artery segmentation, plaque quantification, and fluid dynamics simulation.
Virtual Cell
Integrating multi-omics data and generative AI to simulate cellular dynamics. Creating in-silico models to understand disease progression at the fundamental biological level.
Projects
Team
Z
Zongyuan Ge
Founding Director of the AIM for Health Lab
L
Litao Yang
Research Fellow
Z
Zhenhua Chen
zhenhua.chen@monash.edu
Y
Yunshu Chen
yunshu.chen@monash.edu
C
Chang Yuwen
chang.yuwen@monash.edu
Z
Zhaolin Yu
zyuu0081@student.monash.edu
J
Jason Liu
jason.liu1@monash.edu
About
AIM for Pathology is a research team within the AIM for Health Lab (Augmented Intelligence and Multimodal Analytics for Health), founded and directed by A/Prof. Zongyuan Ge at Monash University. We leverage cutting-edge AI technologies — including large-scale foundation models, vision-language models, and multi-agent systems — to address critical challenges in computational pathology, dental imaging, cardiac CT analysis, and virtual cell simulation.
The broader AIM for Health Lab spans cross-cutting expertise in health AI translation, privacy-preserving AI, federated learning, digital twins, and multimodal data analysis, with deep connections to first-tier healthcare providers and industry partners. Our research has been published in top venues including Nature Medicine, Nature Nanotechnology, Science Advances, The Lancet Digital Health, and leading AI conferences such as NeurIPS, CVPR, and MICCAI.
We are always looking for passionate Ph.D. students, postdocs, and visiting scholars. Feel free to reach out via Zongyuan.Ge@monash.edu.