Kan (Koala)
在資訊管理與計算機科學之間持續漂流探索的無尾熊。從最初投入資訊系統開發與資料分析實作，逐步轉向深度學習與電腦視覺研究，並透過參與開源社群與實務專案，將研究成果延伸至醫療影像與工業視覺檢測等真實應用場景。期望藉由開源協作與人工智慧技術的結合，推動模型從論文走向工程現場，真正解決現實世界中的複雜問題。
A koala continuously drifting and exploring between the fields of Information Management and Computer Science. Starting from hands-on work in information system development and data analytics, the journey gradually shifted toward research in deep learning and computer vision. Through active involvement in open-source communities and practical engineering projects, research outcomes have been extended to real-world application domains such as medical image analysis and industrial visual inspection. By integrating open collaboration with artificial intelligence technologies, the aim is to bridge the gap between academic models and engineering deployment, enabling AI systems to address complex challenges in real-world environments.
Session
In practical artificial intelligence development, while model design is important, project success often depends more on data annotation workflows, toolchain integration, and engineering deployment. This talk draws on my real-world contributions to the open-source annotation tool Labelme, showing how practical user needs led to multilingual localization and feature improvements, as well as the technical trade-offs involved in collaborating with upstream maintainers and the community. Building on this experience, the session explores how open-source tools help bridge AI research and real-world applications, highlighting challenges such as data quality and privacy in medical image segmentation and data distribution shifts and real-time requirements in multi-site industrial vision deployment. Through cross-domain case studies, it emphasizes the critical role of open-source tooling in modern AI workflows and demonstrates how addressing real problems through community collaboration can effectively support engineering practice in medical and industrial AI systems.