AI for Medical Diagnosis and Prediction
Lecturers: Prof. Dr. Shadi Albarqouni
Tutors: TBA
Time: March - April 2025 (tentative)
Course Description
This is an 8-week course designed for non-computer science students at the Lebanese American University. The course equips students with practical skills to implement AI in the field of medical diagnosis and prediction. Students will explore techniques like medical image classification, segmentation, object detection, and reconstruction, as well as time-series data analysis for medical forecasting. Additionally, they will learn about weakly-, semi-, and self-supervised learning, and how to tackle fairness and robustness challenges in healthcare AI.
Course Objectives
By the end of this course, students will:
- Understand the role of AI in medical diagnosis and prediction.
- Apply AI techniques to medical image analysis and time-series forecasting.
- Explore weakly-, semi-, and self-supervised learning in healthcare.
- Analyze ethical implications, fairness, and robustness in healthcare AI models.
- Gain hands-on experience with coding medical AI solutions.
Target Audience
This course is tailored for non-computer science students who have taken introductory courses in Machine Learning and Deep Learning. The students are expected to have an interest in applying AI to medical applications but do not require deep knowledge of computer science concepts.
Course Schedule (12 hours per week)
| Week | Lecture (3 hrs) | Description | Practical Sessions (3 hrs) |
|---|---|---|---|
| Week 1 | Introduction to AI in Healthcare | Overview of AI in healthcare, challenges, and opportunities. | Image classification using CNNs on medical datasets. |
| Week 2 | Medical Image Segmentation | Explore AI segmentation techniques like U-Net. | Implement U-Net for medical image segmentation. |
| Week 3 | Object Detection in Medical Imaging | Object detection models and challenges in medical images. | Implement YOLO/Faster R-CNN on medical datasets. |
| Week 4 | Medical Image Reconstruction & Time-Series Data | AI techniques for image reconstruction and medical time-series. | Time-series classification using RNN/LSTM models. |
| Week 5 | Time-Series Forecasting & Weakly Supervised Learning | Forecasting techniques and weakly supervised learning in healthcare. | Apply forecasting models and weakly labeled data. |
| Week 6 | Semi-Supervised & Self-Supervised Learning | Explore semi- and self-supervised learning. | Train models with limited labels using contrastive learning. |
| Week 7 | Fairness & Robustness in AI for Healthcare | Ethical AI, fairness, and robustness in healthcare AI models. | Analyze bias and robustness in healthcare datasets. |
| Week 8 | Advanced Applications & Final Project Presentation | Explore advanced AI applications and wrap-up project presentations. | Student presentations and peer review of final projects. |
Course Materials
- Lecture slides and readings will be provided weekly.
- Jupyter notebooks for hands-on labs.
Evaluation and Projects
Students will be evaluated through a combination of:
- Predefined Projects: Students will work on predefined medical AI projects, implementing techniques learned throughout the course.
- MCQs: Quizzes covering theoretical aspects of AI and its applications in healthcare.
- Project Presentations: Final presentations of projects in Week 8.
- Peer Reviews: Students will review each other’s projects for additional feedback and learning.
Prerequisites
Students should have a basic understanding of machine learning and deep learning, as well as some experience with Python programming.
Contact Information
For any inquiries, please contact the instructor at Prof. Dr. Shadi Albarqouni.
