This course provides an introductory overview of medical AI, focusing on medical imaging and healthcare data analysis. Students will learn the basic characteristics of medical imaging data and fundamental machine learning and deep learning methods used in medical applications.
• The primary goal is to enable students to:
1. Understand and explain the basic principles of medical imaging and healthcare data, such as MRI, CT, X-ray, and pathology images.
2. Understand fundamental machine learning and deep learning techniques used in medical AI.
3. Apply basic AI methods to representative medical imaging tasks such as classification and segmentation.
4. Recognize recent challenges in medical AI, including limited annotations, data heterogeneity, and domain shift, and understand representative research approaches to address them.

1. Introduction to Medical AI
2. Medical Imaging Modalities (MRI, CT, X-ray, Pathology)
3. Basics of Machine Learning for Medical Data
4. Neural Networks & Deep Learning Basics / Medical Image Representation
5. Convolutional Neural Networks / Applications in Medical Imaging
6. Medical Image Classification
7. Medical Image Segmentation
8. Midterm exam
9. Medical Image Enhancement & Reconstruction
10. Image Registration & Multimodal Data Alignment
11. Limited Labels and Data Scarcity / Weakly-Supervised and Self-Supervised Learning
12. Domain Shift and Data Heterogeneity / Multicenter Medical Data Analysis
13. Recent Research Trends in Medical AI / Foundation and Multimodal Models
14. Model Interpretation & Failure Analysis
15. Open Challenges in Medical AI / Future Research Directions
16. Final exam

Lecture, Hands-on Implementation