生醫資料、訊號及影像的人工智慧
Artificial Intelligence for Biomedical Data, Signal, and Image
| 節 | 週二 |
|---|---|
5 13:20–14:10 | 生醫資料、訊號及影像的人工智慧 YR101（陽明） 2 節連堂 |
6 14:20–15:10 |
* 根據陽明交大上課時間表所列
The course is designed to provide students with a comprehensive understanding of machine learning and deep learning theories that are specifically tailored for practical applications. The course will delve into the realm of AI and its potential applications in healthcare and will feature case studies that demonstrate real-world applications such as infoepidemiology during and post-COVID era, the hearing smart medical system, which leverages AI algorithms to analyze patient data and provide personalized treatment plans based on their hearing loss. The course will also demonstrate how AI can be used to predict heart failure risk from hemodialysis data and how mass spectrometry-based proteomics can be used. By using medical data examples, the course will highlight the potential and limitations of these techniques and provide students with a deep understanding of how these techniques can be applied in real-world settings. The course will also explore real-world implementations that involve various data processing methods, model development, and optimization strategies, providing students with a hands-on approach to learning. Overall, the course is an excellent opportunity for students to gain a comprehensive understanding of AI and its potential healthcare applications and explore the latest developments in machine learning and deep learning theories tailored for practical applications.
• Basic concept of Python programming
無備註
This course has been designed to offer practical exposure to a range of cutting-edge machine-learning models and signal-processing techniques.
期中報告: 30% 期末專題: 40% 出席: 10% 作業: 20% Midterm Report: 30% Final Project: 40% Attendance: 10% Homework: 20%
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Traditional statistics vs. AI 2026-09-08(二) 時數：[2026-09-08]吳俊穎(2.00) |
| 第 2 週 | Preprocessing health big data for AI models 2026-09-15(二) 時數：[2026-09-15]吳俊穎(2.00) |
| 第 3 週 | Transitioning from artificial neural networks (ANN) to convolutional neural networks (CNN) 2026-09-22(二) 時數：[2026-09-22]Gupta Pushpanjali(2.00) |
| 第 4 週 | Infoepidemiology: Insights from the internet Search Trends in the COVID-19 pandemic 2026-09-29(二) 時數：[2026-09-29]蘇家玉(2.00) |
| 第 5 週 | Infoepidemiology: Impacts of Mental Health and Long-COVID Symptoms in the Post-COVID era 2026-10-06(二) 時數：[2026-10-06]蘇家玉(2.00) |
| 第 6 週 | Traditional vs. customized CNN models 2026-10-13(二) 時數：[2026-10-13]Sulagna Mohapatra(2.00) |
| 第 7 週 | Transfer learning and development of models 2026-10-20(二) 時數：[2026-10-20]Sulagna Mohapatra(2.00) |
| 第 8 週 | Mid-term report 2026-10-27(二) 時數：[2026-10-27]吳俊穎(2.00) |
| 第 9 週 | Ensemble learning: Integration of image and clinical data 2026-11-03(二) 時數：[2026-11-03]Gupta Pushpanjali(2.00) |
| 第 10 週 | Developing hearing smart medical system: from prototyping to aging hearing signal and public health 2026-11-10(二) 時數：[2026-11-10]朱原嘉(2.00) |
| 第 11 週 | Introduction to Mass Spectrometry-Based Proteomics 2026-11-17(二) 時數：[2026-11-17]巫坤品(2.00) |
| 第 12 週 | Techniques for model evaluation and addressing imbalanced data 2026-11-24(二) 時數：[2026-11-24]吳俊穎(2.00) |
| 第 13 週 | Radiomics feature analysis 2026-12-01(二) 時數：[2026-12-01]吳俊穎(2.00) |
| 第 14 週 | Real-time Analysis of Massive Continuous Data from a Dialysis Machine Signal to Predict Heart Failure Risk with New AI Platform 2026-12-08(二) 時數：[2026-12-08]朱原嘉(2.00) |
| 第 15 週 | AI-enhanced healthcare ethical and legal issues 2026-12-15(二) 時數：[2026-12-15]吳俊穎(2.00) |
| 第 16 週 | Final report presentation 2026-12-22(二) 時數：[2026-12-22]吳俊穎(2.00) |
Teaching materials and appointment articles
- 地點
- 守仁樓數位醫學中心
- 時間
- Tuesday 12:00-13:00.
- 聯絡方式
- Email聯繫: cywu4@nycu.edu.tw Prof. Chun-Ying Wu