AI for Medical Diagnosis Course Syllabus

Overview: This course provides a practical introduction to applying deep learning in medical diagnosis, focusing on real-world applications in medical imaging. You'll learn to build and evaluate convolutional neural networks for disease detection in chest X-rays and 3D MRI scans. The curriculum emphasizes hands-on learning through coding assignments and projects, with a focus on overcoming challenges like class imbalance and limited datasets. Total time commitment is approximately 28 hours, designed for flexible, self-paced study on Coursera.

Module 1: Disease Detection with Computer Vision

Estimated time: 8 hours

• Introduction to medical image diagnosis using deep learning
• Building and training CNN models for disease classification
• Handling class imbalance in medical datasets
• Multi-task learning for detecting multiple pathologies

Module 2: Evaluating Models

Estimated time: 4 hours

• Implementing evaluation metrics for medical AI models
• Understanding sensitivity, specificity, and AUC
• Techniques for model validation in clinical contexts
• Testing models under real-world diagnostic constraints

Module 3: 3D Medical Imaging

Estimated time: 8 hours

• Working with 3D MRI scans for brain disorder diagnosis
• Applying 3D CNNs to volumetric data
• Segmentation techniques for 3D medical images
• Addressing challenges in processing large volumetric datasets

Module 4: Addressing Data Challenges

Estimated time: 4 hours

• Strategies for working with limited medical data
• Data augmentation specific to medical imaging
• Transfer learning in low-data regimes

Module 5: Best Practices in Medical AI

Estimated time: 4 hours

• Training deep learning models with clinical reliability
• Validating models for healthcare applications
• Evaluating models for safety and fairness

Module 6: Final Project

Estimated time: 8 hours

• Diagnose lung disorders using chest X-ray images
• Apply 3D CNNs to classify brain disorders from MRI scans
• Submit a comprehensive model evaluation report

Prerequisites

• Familiarity with deep learning concepts and neural networks
• Proficiency in Python programming
• Basic understanding of machine learning frameworks like TensorFlow or PyTorch

What You'll Be Able to Do After

• Develop CNN models for medical image classification
• Diagnose lung and brain disorders using X-rays and MRI scans
• Address class imbalance and data scarcity in medical datasets
• Apply best practices in training and validating medical AI models
• Evaluate model performance using clinical metrics like sensitivity and specificity