Course Overview
The integration of artificial intelligence in medical imaging is revolutionizing diagnostics and treatment, driven by the emergence of powerful foundation models. This course explores the cutting-edge intersection of AI and healthcare, offering you the skills to shape the future of medical technology.
You'll learn the principles and applications of foundation models in medical imaging, gaining hands-on experience in developing innovative solutions to improve human health. Through practical exercises and real-world case studies, you'll master the techniques needed to enhance diagnostic accuracy and optimize treatment planning.
By the end of this course, you'll be well-equipped to:
- Understand and apply the fundamental principles of foundation models in medical imaging
- Develop and implement AI solutions that push the boundaries of healthcare technology
- Contribute meaningfully to the advancement of diagnostic and treatment practices
Join us to become a pioneer in this rapidly evolving field, where your expertise can directly impact and improve patient outcomes.
What you will learn in this course
This course provides a blend of theoretical knowledge and practical insights, ensuring participants gain a comprehensive understanding of the regulation and its implications for their business operations.
- Introduction to Modern Medical Imaging
- Introduction to medical computer vision (image analysis for medical imaging)
- Introduction to foundation models in general
- Historical evolution of medical imaging
- Types of medical imaging modalities (OCT/fundus, MRI/CT, chest X-ray, ultrasound, etc.)
- Basics of image processing and analysis
- Key challenges in medical imaging
- Role of AI and machine learning in modern medical imaging
- Overview of current trends and future directions in medical imaging
- Applications of Medical Imaging Foundation Models
- Introduction to medical imaging foundation models
- Extracting features with foundation models to perform downstream tasks such as classification, segmentation, tracking and survival analysis
- Introduction to multimodal foundation models, i.e. combining imaging features with non-imaging features (e.g. tabular EHR data, omics, etc.)
- Clinical Machine Learning
- How clinical machine learning differs from non-regulatory machine learning
- Regulatory concerns and software as a medical device (SaMD)
- Interpretability, uncertainty quantification, and calibration
- Model evaluation and validation
- Stress testing and randomized controlled trials (RCT) reporting guidelines
- Ethical and bias considerations in clinical ML
- Foundation Model Ops (FMOps)
- Introduction to foundation model ops
- Foundational model systems
- Scalability and performance
- Foundational models in production
- Monitoring and maintenance
- Security and privacy
- Ethical considerations
- Case studies and real-world applications
What you will achieve in this course
By completing this course, learners will achieve:
- How to use off-the-shelf medical foundation models for image analysis
- How to build applications based on foundation models
- The basics of the regulatory landscape
Prerequisites
You should be familiar with machine learning and Python, ideally with prior computer vision experience.
Who is this course for
This course is designed for:
- Professionals and researchers in healthcare interested in integrating AI and machine learning into medical imaging.
- Data scientists and engineers seeking to specialize in deep tech applications within healthcare.
- Students and academics aim to advance their knowledge in modern medical imaging and clinical machine learning.
- Individuals looking to explore and contribute to innovations in AI-driven diagnostics and treatment planning in healthcare.
