A Systematic Review of Few-Shot Learning in Medical Imaging

TL;DR

This systematic review analyzes how few-shot learning, especially meta-learning, addresses data scarcity in medical imaging, highlighting its effectiveness across various medical outcomes and anatomical regions.

Contribution

It provides a comprehensive overview of recent few-shot learning methods in medical imaging, identifying trends, pipelines, and the effectiveness of meta-learning techniques.

Findings

Few-shot learning overcomes data scarcity in medical imaging.

Meta-learning is the most popular and adaptable approach.

Supervised and semi-supervised learning are highly effective.

Abstract

The lack of annotated medical images limits the performance of deep learning models, which usually need large-scale labelled datasets. Few-shot learning techniques can reduce data scarcity issues and enhance medical image analysis, especially with meta-learning. This systematic review gives a comprehensive overview of few-shot learning in medical imaging. We searched the literature systematically and selected 80 relevant articles published from 2018 to 2023. We clustered the articles based on medical outcomes, such as tumour segmentation, disease classification, and image registration; anatomical structure investigated (i.e. heart, lung, etc.); and the meta-learning method used. For each cluster, we examined the papers' distributions and the results provided by the state-of-the-art. In addition, we identified a generic pipeline shared among all the studies. The review shows that few-shot learning can overcome data scarcity in most outcomes and that meta-learning is a popular choice to perform few-shot learning because it can adapt to new tasks with few labelled samples. In addition, following meta-learning, supervised learning and semi-supervised learning stand out as the predominant techniques employed to tackle few-shot learning challenges in medical imaging and also best performing. Lastly, we observed that the primary application areas predominantly encompass cardiac, pulmonary, and abdominal domains. This systematic review aims to inspire further research to improve medical image analysis and patient care.