選課日期：114/03/16截止 Course selection deadline: March 16, 2025.
課程名稱
Course’s Name
AI醫學影像分析實作 AI for Medical Image Analysis
（對應總課程名稱：跨域AI實作微學分課程 AXB AI Co-Teaching Program）
授課教師
Lecturer information
電子研究所 賴伯承 教授
腦科學研究所 紀以柔 博士生
電機資訊國際博士學位學程 冉恩達 博士生
課程概述與目標
Course Overview and Objectives
This course explores the practical applications of artificial intelligence in medical image analysis, focusing on deep learning techniques, image preprocessing, model training, and evaluation. Participants will gain hands-on experience with AI models for various imaging modalities, including MRI, CT, X-ray, Histopathology, and retinal scans. The course will also cover key challenges such as model interpretability, bias mitigation, and regulatory considerations in medical AI.
By the end of the course, participants will:
- Understand the fundamentals of different types of medical images and AI-driven medical image analysis, including deep learning architecture and preprocessing techniques.
- Develop proficiency in implementing and fine-tuning AI models for different medical imaging tasks.
- Evaluate model performance using appropriate metrics and interpret AI-generated results.
- Identify and address challenges related to data bias, fairness, and regulatory compliance in medical AI applications.
Gain hands-on experience with AI frameworks and tools commonly used in medical image analysis.
課程日期
Course’s Day
3/22 13:00-16:00 PM
3/29 13:00-15:00 PM
3/23 13:00-16:00 PM
課程總時數
Total Course Hours
8小時
上課地點
Class Location
交大校區EA101
修課人數上限
Maximum number of applicants
15人
先修科目或先備能力
Preparatory Abilities Review Standards
Please complete the pre-course survey: Google form .
學生自備物品
Items provided by students
| laptop |
作業、考試、評量
Assignments, Exams, Assessments
Labs and homework (done individually) 20%
Final project (done in groups of 1-3 members) 80%
課程大綱
Course Outline
| 單元主題Topic | 內容綱要Content outline | 講授Teach | 示範Demonstration | 習作Exercises | 其他Others |
| Principles and Diagnosis Overview | Overview of medical imaging modalities and principles, introduction to example diseases (autism, pneumonia, dementia, diabetes mellitus…) in class | 1hr | 0hr | 0hr | 0hr |
Data preprocessing | Focus on how to preprocess with Python to make into Pytorch dataloader Explain two examples: MRI and Histo Data preprocessing lab: give each team a dataset and prepare a pytorch dataloader (will use later for group project) | 0.5h | 0.5hr | 1hr | 0hr |
| Computer Vision with Deep Learning Introduction | This session covers the fundamentals of computer vision and deep learning, including structures of models, feature extraction, image classification, and hands-on implementation of medical image analysis models. | 0.5hr | 0.5hr | 0hr | 0hr |
| Image Classification Colab Tutorial | This session provides a hands-on tutorial using Google Colab to implement image classification models, covering data preprocessing, model training, evaluation, and visualization of results. | 0hr | 0.5hr | 0.5hr | 0hr |
| Results Interpretation and Reasoning | This session focuses on interpreting AI model results by integrating medical knowledge of diseases and clinical contexts, analyzing performance metrics, visualizing decision-making processes, and making informed inferences to support clinical decision-making. | 0.5hr | 0.5hr | 0hr | 0hr |
| Group project presentations | This session features group project presentations where participants apply and integrate the skills learned throughout the course, showcasing their end-to-end AI model development, from data preprocessing and training to result interpretation and clinical reasoning. | 0hr | 0hr | 2hr | 0hr |
課程進度表
Course Schedule
| 日期 Date | 課程進度、內容、主題 Course progress, content, topics | |
| 3/22 | 1.1 Introduction to medical imaging modalities (MRI, CT, X-ray, Retinal Imaging, Histopathology) 1.2 Fundamental principles behind different imaging techniques 1.3 Common medical conditions analyzed through imaging (e.g., tumors, cardiovascular diseases, retinal disorders; autism, pneumonia, dementia) 1.4 Role of AI in medical image diagnosis and clinical decision-making 2.1 Importance of data preprocessing in AI-driven medical imaging 2.2 Image normalization, augmentation, and enhancement techniques 2.3 Handling missing data and noise reduction methods 2.4 Dataset loading, preprocessing, and augmentation in Python | |
| 3/23 | 3.1 Fundamentals of computer vision and its applications in healthcare 3.2 Overview of deep learning architectures for medical imaging (CNNs, transformers, hybrid models) 3.3 Feature extraction and representation learning in medical image analysis 3.4 Challenges and limitations of deep learning in clinical practice 4.1 Hands-on implementation of image classification models using Google Colab 4.2 Training deep learning models for disease classification (e.g., normal vs. abnormal scans) 4.3 Performance evaluation with key metrics (accuracy, precision, recall, AUC-ROC) 5.1 Understanding AI model predictions and performance metrics 5.2 Visualization techniques (Grad-CAM) for explainable AI in medical imaging 5.3 Integrating clinical knowledge with AI results for diagnosis and decision-making 5.4 Ethical considerations and bias detection in medical AI applications | |
| 3/29 | 6.1 Presentation of team-based AI projects on medical image analysis 6.2 Demonstration of end-to-end model development, from preprocessing to interpretation 6.3 Peer review and feedback session to improve clinical applicability of AI models 6.4 Discussion on challenges, lessons learned, and future improvements |
課程教材
Course’s Materials
1. A. Sweigart, Automate the Boring Stuff with Python, 2nd Ed., No Starch Press, Dec. 2019
2. Literature reading.
3. Online resources.
微學分課程Q&A
📌 我想修微學分課程，該如何報名/進行選課？
每門課程將於開課前三週至前一週間開放選課，請同學於選課截止日前至ICT選課系統進行報名。
📌 若是學期課加開的微學分課程，我可以修學期課又修微學分嗎？
完全不可。學生不得同一學期選修學期課及微學分，僅能二擇一。微學分若成功認計僅可計於【自由學分】，而非【必修學分】。
補充說明：學期課是學校課務系統上選的3學分課程，微學分則是在ICT系統上報名的課程，兩者的永久課號不同。有的學期課微學分將會分成-1,-2,-3或A,B,C三階段來進行，每完成一階段，需再報名下一階段選課。若是課程內容設計具連貫性，則未修-1或A者，不得選修-2,-3或B,C課程。
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校外學生請點擊ICT選課系統頁面右上角【非本校學生申請賬號】，完成註冊後可以進入系統選課。若該課程不開放給校外學生選課，則無法進行報名。
📌 若已經畢業/在職中，可以選課嗎？
校內/校外的【非學生】身份者，請在選課前寄信詢問授課教師是否可以參與課程，獲教師同意後再進行選課報名，修課名額將以【校內在學學生】為優先。
📌 如何得知是否選上了該課程？
請於選課截止的隔日至選課系統查詢選課結果。
📌 我已經選了課程，該如何退選？
① 若選課報名未截止，可直接在ICT選課系統上點擊【取消報名】。
② 若報名已截止至開課日前，請寄信給助理corlyn@nycu.edu.tw 告知退選意願並說明原因。
③ 若開課當日/課程已進行中，請寫信給授課教師，經老師同意後，截圖/轉寄信件內容給助理。
📌 若沒有退選/無故缺席課程會如何？
未依規定辦理課程退選或無故未到課，將取消該同學兩月內之ICT選課權益，即2個月內無法報名選課系統上的任何課程。
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若學生因故無法出席課程，請於上課前兩日寫信給授課教師說明請假原因(請附上姓名、學號及請假事由)。
📌 我是學士/碩士/博士生，可以報名選修微學分課程嗎？
可以。惟請留意該學分是否認計為畢業學分，將由您的系所決定。為避免學分爭議，請於抵免前詢問系所該門課的學分是否可以認計。研究生修習之微學分不得採計於畢業學分。
📌 修微學分課程需要繳學分費嗎？
無需。
📌 課程會有成績嗎？
微學分的修課方式比照一般課程，必須通過課堂之作業、測驗、討論、實驗或成果發表等教學活動規定，經授課教師認證，該修課結果通過或不通過。
📌 微學分完修證明該如何申請？
請至微學分課程頁面查詢完修證明申請表，並於結課日前提交申請，詳細規定請參閱表單內容。
📌 申請完修證明就表示匯出學分了嗎？
否，兩者是分開的，授課教師提供成績後，由創創工坊核檢學生修課結果，確認同學【通過】課程後，會在2-4週內以電子檔寄出。而學分登錄表，則需到選課系統下載。
📌 修課結果會直接出現在成績單上嗎？
否，請同學必須於畢業當學期統一提出抵免申請。請至ICT選課系統上【匯出學分登錄表】，經系所認計及各單位簽核完成後，該成績才會出現在成績單上。