Deep Learning with PyTorch for Medical Image Analysis Course Syllabus

This course provides a hands-on introduction to deep learning for medical image analysis using PyTorch. Designed for beginners with foundational Python and deep learning knowledge, it covers end-to-end workflows including data preprocessing, model building, training, and evaluation across key tasks such as classification, segmentation, and detection. With approximately 10 hours of content, learners will gain practical experience working with real-world medical imaging formats and build a final project applying all learned skills.

Module 1: Introduction to Deep Learning & PyTorch

Estimated time: 0.75 hours

• Overview of deep learning basics and medical imaging context
• PyTorch setup and environment configuration
• Introduction to tensors and basic operations in PyTorch

Module 2: Medical Imaging Formats & Preprocessing

Estimated time: 1 hour

• Introduction to DICOM and NIfTI formats
• Data loading using pydicom and nibabel libraries
• Image normalization, resizing, and augmentation techniques for medical datasets

Module 3: Convolutional Neural Networks (CNNs)

Estimated time: 1 hour

• Building basic CNN models in PyTorch
• Understanding activation functions and pooling layers
• Backpropagation in convolutional networks

Module 4: Classification with CNNs in Medical Imaging

Estimated time: 1.25 hours

• Training CNNs for binary and multi-class medical image classification
• Evaluation metrics: accuracy, AUC, confusion matrix
• Implementing training loops and validation pipelines

Module 5: Semantic Segmentation

Estimated time: 1.5 hours

• U-Net and encoder-decoder architectures
• Pixel-level segmentation in medical scans
• Implementing and training segmentation models in PyTorch

Module 6: Object Detection & Localization

Estimated time: 1 hour

• Bounding box techniques in medical imaging
• Integrating object detection with classification
• Building combined detection and localization pipelines

Module 7: Model Evaluation & Optimization

Estimated time: 0.75 hours

• Loss functions and regularization techniques
• Overfitting control and performance tuning
• Saving models, checkpointing, and inference optimization

Module 8: Project: End-to-End Medical Image Analysis

Estimated time: 1.5 hours

• Applying techniques on a real medical dataset
• Full workflow: data loading → preprocessing → training → evaluation
• Generating predictions and interpreting model outputs

Prerequisites

• Basic understanding of Python programming
• Familiarity with deep learning fundamentals
• Experience with neural networks and machine learning concepts

What You'll Be Able to Do After

• Understand deep learning principles in the context of medical imaging
• Implement CNNs using PyTorch for classification, segmentation, and detection tasks
• Work with DICOM, NIfTI, and other medical imaging formats
• Apply preprocessing and augmentation techniques to medical datasets
• Build full end-to-end pipelines for real-world medical image analysis