Radiologist-in-the-loop AI
Emphasizing the synergy of radiologists' and machines' intelligence to enhance the performance of radiological AI systems!
Explore the power of collaboration between radiologists and machine learning researchers
Learning Objectives
- Design a radiologist-in-the-loop AI system architecture
- Use various active learning strategies to sample data for radiologists’ annotation
- Create labeling instructions for knowledge transfer
- Apply the new approach to a real-world problem of liver view classification in ultrasound images (i.e., images with liver vs. without liver)
What You Will Learn
- What is radiologist-in-the-loop AI?
Describes how radiologists and machine learning algorithms interact to solve radiological AI problems.
- Introduction to Active Learning (AL)
Introduces the concept of AL and how it differs from a random approach by using a systematic method for selecting data to be annotated.
- Uncertainty sampling
Describes strategies for identifying images that are near a decision boundary in the current ML model.
- Diversity sampling
Describes strategies for identifying unlabeled images that are underrepresented in the current ML model.
- PyTorch implementation of the sampling strategies
Demonstrates how to implement the image sampling strategies and apply them to real-world problems.
- Prepare clear labeling instructions
Explains how knowledge-transfer can be achieved.
- Liver view classification
How the concepts learned above can be used to perform liver view classification in ultrasound images (i.e., images with liver vs. images without liver).
Instructor
Research Fellow
Harvard Medical School/Massachusetts General Hospital
aali25 [at] mgh.harvard.edu
