Deep Learning Fundamentals for Healthcare
2h 26mIntermediate2025-04-16
Authors
Wuraola Oyewusi
Wuraola Oyewusi is an experienced data scientist, machine learning, and artificial intelligence professional.
Course details
Explore the exciting world of deep learning applications in healthcare through this in-depth course. Learn how to classify and detect abnormalities in X-ray images through convolutional neural networks (CNNs), fine-tuning pre-trained models, and leveraging zero-shot learning. Understand the basics of deep learning, including neural networks, model training, and hyperparameter tuning tailored specifically to healthcare. Engage in hands-on activities where you'll preprocess data, build models with Python, and utilize frameworks like TensorFlow and PyTorch. Develop practical skills in object detection and segmentation to diagnose and detect medical conditions effectively. Gain insights into ethical considerations and data limitations pertinent to applying AI in a medical context. By the end of this course, you will be equipped to apply deep learning techniques to real-world healthcare challenges, improving diagnostic accuracy and patient outcomes.
Learning objectives
Understand the intricacies of deep learning and how to implement it in a healthcare context.
Build confidence in core competencies like hyperparameter tuning and fine-tuning an existing model on a new task.
Navigate the health-focused pretrained model ecosystem and learn how to iterate to find the one that works well for your use case.
Learning objectives
Understand the intricacies of deep learning and how to implement it in a healthcare context.
Build confidence in core competencies like hyperparameter tuning and fine-tuning an existing model on a new task.
Navigate the health-focused pretrained model ecosystem and learn how to iterate to find the one that works well for your use case.
Skills covered
IT Service ManagementNeural Networks and Deep LearningDevOpsArtificial Intelligence (AI)Network and System AdministrationOne-Off
Concepts
Introduction
- Getting started with deep learning for healthcare
- What you should know
Introduction to Deep Learning for Healthcare
- What is deep learning
- Why deep learning excels in healthcare
- How deep learning works - Anatomy of neural networks
- Deep learning architectures
- Deep learning algorithms
- Key concepts in training deep learning models
- Deep learning frameworks and libraries
- Building and implementing neural networks - TensorFlow
- Building and implementing neural networks - PyTorch
- Limitations and ethical considerations
Computer Vision for Healthcare
- Introduction to computer vision in healthcare
- Understanding image data in healthcare
- Anatomy of medical images - Exploring digital properties and structure
- Preparing X-ray image datasets for computer vision tasks
- Task overview - Classifying normal vs. pneumonia X-rays
- Classifying normal vs. pneumonia X-rays using convolutional neural networks (CNNs)
- Classifying normal vs. pneumonia X-rays using a pretrained architecture
- Classifying normal vs. pneumonia X-rays using zero-shot learning
- Task overview - Object detection in medical imaging
- Zero-shot object detection in X-rays using CLIP-based models
Conclusion
- Next steps