Qiaoyu Zheng [郑乔予]

Qiaoyu Zheng (郑乔予)

Hello! I am a PhD. student at Shanghai Jiao Tong University, advised by Prof. Weidi Xie. I graduated with a bachelor's degree in Computer Science from the School of Electronic Information and Electrical Engineering at Shanghai Jiao Tong University in June 2023.

My current research interest focuses on Artificial Intelligence for Healthcare (AI4Health). I am looking forward to the day when AI in healthcare can truly benefit humanity.

Email / Github / Google Scholar

Research

	M³Builder: A Multi-Agent System for Automated Machine Learning in Medical Imaging Jinghao Feng, Qiaoyu Zheng, Chaoyi Wu, Ziheng Zhao, Ya Zhang, Yanfeng Wang, Weidi Xie† Technical Report, 2025. We present M³Builder, an LLM-powered multi-agent system for autonomous end-to-end medical imaging AI model training—the first to automate ML in medical imaging, lowering the threshold for clinicians to develop and apply AI models, and promoting the widespread adoption of AI tools in real clinical scenarios.
	Can Modern LLMs Act as Agent Cores in Radiology Environments? Qiaoyu Zheng, Chaoyi Wu, Pengcheng Qiu, Lisong Dai, Ya Zhang, Yanfeng Wang†, Weidi Xie† Technical Report, 2024. We evaluates modern LLMs as agent cores in radiology environments through a comprehensive dataset, innovative evaluation platform, and performance assessments of leading models. Results highlight key challenges in tool comprehension, information synthesis, and format maintenance, confirming that while promising, current LLMs are not yet ready to serve as standalone radiology agent cores.
	Large-scale Long-tailed Disease Diagnosis on Radiology Images Qiaoyu Zheng, Weike Zhao, Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang†, Weidi Xie† Nature Communications, 2024.* build up an academically accessible, large-scale diagnostic dataset, present a knowledge enhanced model architecture that enables to process arbitrary number of input scans from various imaging modalities, and initialize a new benchmark for multi-modal multi-anatomy long-tailed diagnosis. Our method shows superior results on it. Additionally, our final model serves as a pre-trained model, and can be finetuned to benefit diagnosis on various external datasets.
	Can GPT-4V(ision) Serve Medical Applications ? Case Studies on GPT-4V for Multimodal Medical Diagnosis Chaoyi Wu, Jiayu Lei, Qiaoyu Zheng, Weike Zhao, Weixiongt Lin, Xiaoman Zhang, Xiao Zhou, Ziheng Zhao, Ya Zhang, Yanfeng Wang , Weidi Xie† Technical Report, 2023. We present recent efforts on assessing GPT-4V for multimodal medical diagnosis, by case studies, covering 17 human body systems, across 8 clinical imaging modalities, e.g., radiology, pathology.