This advanced graduate course covers the application of deep learning techniques to medical imaging and biomedical signal processing. Students learn to apply state-of-the-art neural network architectures to real-world medical problems.

Course Topics

• Convolutional Neural Networks for Medical Imaging
• Time Series Analysis for Biomedical Signals
• Transformers in Medical AI
• Self-supervised and Contrastive Learning
• Long-tailed and Imbalanced Medical Data
• Clinical Decision Support Systems

Projects

Students complete hands-on projects involving:

• MRI image analysis for neurological disorders
• EEG signal processing for cognitive load assessment
• Development of AI models for clinical decision support

Assessment

• Research project (40%)
• Midterm examination (25%)
• Final examination (25%)
• Class participation and assignments (10%)