Computer Vision

Few-Shot Medical Diagnosis with Model-Agnostic Meta-Learning

Optimizing Diagnosis in Sparse Data Environments: A Model Agnostic Meta Learning Approach

20-shot adaptation

Medical diagnosis when labelled images are scarce: a model that learns how to learn, then adapts from a handful of examples.

The problem

Many diagnostic tasks simply do not have large labelled datasets, which limits conventional deep learning.

The approach

Model-Agnostic Meta-Learning with a 20-shot training strategy on the NIH Chest X-ray dataset, so the model adapts quickly to new tasks.

The outcome

Competitive diagnostic accuracy and robust performance from very limited data.

In detail

Applied Model-Agnostic Meta-Learning to medical image diagnosis under severe data scarcity. Using the NIH Chest X-ray dataset, the project evaluates a 20-shot learning strategy designed to adapt quickly to new diagnostic tasks from only a small number of labelled examples. The work demonstrates how meta-learning can support medical AI in settings where assembling large annotated datasets is impractical.

Built with

• Medical Imaging
• Few-Shot Learning
• MAML

Source

• Optimizing Diagnosis in Sparse Data Environments: A Model Agnostic Meta Learning Approach, 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024

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By Kanishka Ranaweera, PhD, Machine Learning Engineer in Melbourne, Victoria, Australia. hello@kanishkabuilt.me