- 描述 In medical domain, the scarcity and sensitivity of imaging data present significant challenges to the development of accurate and privacy-preserving segmentation models. In this paper, an Optimized Training framework with Trustworthy Enhanced Replication (OTTER) via diffusion and federated VMUNet is proposed to solve these problems. OTTER leverages diffusion-based generative models to synthesize diverse and high-fidelity medical images that reflect the statistical distribution of real data while avoiding direct exposure of patient information. These synthetic samples are further filtered using a Bayesian quality estimator to ensure training on only reliable and representative data. These trusted replicas are integrated into a federated learning pipeline built on VMUNet, a state-space-based segmentation architecture that enables decentralized training across medical centers without sharing raw data. Experiments on multiple public medical image segmentation datasets have demonstrated that OTTER achieves competitive or superior performance in terms of accuracy, robustness, and generalization, while maintaining strong privacy guarantees. Our results highlight the potential of combining generative data augmentation with federated architectures to advance privacy-aware medical AI.
- Haocheng Kan, Yuesheng Zhu, Guibo Luo, and Hanwen Zhang. OTTER: Optimized Training with Trustworthy Enhanced Replication via Diffusion and Federated VMUNet for Privacy-Aware Medical Segmentation [C]// Proceedings of the 27th International Conference on Information and Communications Security (ICICS 2025). Nanjing, China: Springer Nature, October 2025. (CCF-C) DOI: 10.1007/978-981-95-3543-9_18
- 關鍵字 Federated Learning 、Privacy-Preserving 、Diffusion Models 、Medical Image Segmentation
- Link https://dl.acm.org/doi/10.1007/978-981-95-3543-9_18
- 描述 該研究專案開發了一個交互式學業成績分析系統，並分析 2008 年以後進入龍華科技大學管理學院的學生在不同類型的課程中的表現，包含他們的經濟狀況和畢業後的就業情況進行分析。 從而可以根據個別學生的初始情況預測畢業後的職業發展。
- Hao-Cheng Kan & Fang-Ling Lin (10 Dec 2016). The Impact of Student Loans on Academic Performance: A Cluster and Visual Analysis. presented at the 22th CSIM IMP, Taichung, Taiwan.
- 程式語言和軟體使用 R、 RShiny Server、 MariaDB 和 Ubuntu Server
中國企業去槓桿化對總體經濟的影響 (Jan 2019 – Dec 2019)
- 描述 本研究首先以動態隨機一般均衡模型 (Dynamic Stochastic General Equilibrium; DSGE) 來分析中國自 1992 年至 2017 年各季度總產值、年利率和通貨膨脹率對於中國整體經濟社會的波動性。接著再以向量自迴歸模型 (Vector Autoregression Model; VAR ) 進行分析，從而得知非金融企業去槓桿對總體經濟成長率短期會有負面影響，過程隨時間的增加影響減小。長期而言，非金融企業去槓桿對總體經濟增長率影響極小，故建議中國政府在進行去槓桿應在短期時配合適當財政或貨幣政策，以降低去槓桿所產生的經濟成長率衝擊降低，來達到穩定經濟的目的。
- 關鍵字 動態隨機一般均衡模型、向量自迴歸模型、濾波法、 總體經濟、景氣循環理論、巴塞爾協定 III、影子銀行、去槓桿
S-Gate 行動教學系統 (Aug 2013 – Aug 2015)
- 描述 本專案基於電子校園的概念設計了一個行動教學系統，具有兩種功能。 第一部分簡化了學生事務和課堂管理的操作，啟用了藍牙點名等活動。 第二部分則是實現了使用手機設備進行實時課堂互動，比如教學內容交付、評估、小組討論、問答等功能。
- 程式語言和軟體使用 Node.js、 JS、 PHP 、 MariaDB 、 Android 和 Ubuntu Server
