Worked on FedPMR, a personalized prototype-based federated learning framework for multi-center accelerated MRI reconstruction. The system supports cross-institution model collaboration while keeping raw medical data local.

Result: Validated on three public datasets and one private dataset from The First Affiliated Hospital, Zhejiang University. Communication cost was reduced from 236.70 Mb to 8 Kb, with stronger reconstruction performance on heterogeneous sites.

Authors: Jing Wang, Tong Wang, Siyuan Luo, Xiaoran Guo, Jiayi Yin, Jing Li, Zehua Yuan, Gang Yu, Bo Lin.

提出一种面向临床多源异构数据的冠心病多模态风险预警方法。针对心电信号（ECG）、超声心动图视频与结构化临床表格数据三类异构模态，设计独立的深度特征提取网络，并通过 InfoNCE 对比学习实现跨模态特征空间对齐。

Innovation：引入基于可学习标识符的模态丢弃策略解决真实临床中的整模态缺失问题，结合交叉注意力机制进行特征动态加权融合，最终通过深度生存分析输出个体化动态生存曲线，为临床分诊提供时间维度的量化依据。

Field：数字医疗 · 医学人工智能 · 多模态深度学习

一个面向 AI 生成图像策略评估的在线盲测平台，用于把“看图打分、方案对比、结论导出”这类评审流程产品化。

Built a lightweight web-based blind testing platform for evaluating AI-generated images across multiple strategies. Reviewers can score anonymous options, add quick labels and comments, then reveal strategy identities after review.

Product value: Turns subjective visual comparison into structured, exportable evaluation data, making model and strategy discussions easier to align across product, design, and technical teams.

Built a knowledge-base Q&A system over PDF, DOCX, and Markdown documents using OpenAI APIs.

Design: Combined semantic chunking, embeddings, vector retrieval, and prompt design to reduce omissions and hallucinations in complex document Q&A, turning raw materials into an interactive knowledge base.

Designed a FedAvg-based multi-client training framework for privacy-sensitive behavior recognition, enabling model collaboration without moving local data.

Balance: Focused on the tradeoff between model performance, data locality, privacy protection, and deployability.