Meta-learning in healthcare: A survey

TL;DR

This survey reviews how meta-learning techniques are applied in healthcare to address data scarcity, domain shifts, and generalization issues, highlighting methods, challenges, and future directions.

Contribution

It provides a comprehensive overview of meta-learning applications in healthcare, categorizing approaches and discussing challenges and future research avenues.

Findings

Meta-learning effectively addresses healthcare data challenges.

Various meta-learning methods are categorized into multi/single-task and few-shot learning.

Identifies key challenges and potential solutions in applying meta-learning to healthcare.

Abstract

As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model's capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional learning approaches, such as insufficient number of samples, domain shifts, and generalization. These unique characteristics position meta-learning as a suitable choice for developing influential solutions in various healthcare contexts, where the available data is often insufficient, and the data collection methodologies are different. This survey discusses meta-learning broad applications in the healthcare domain to provide insight into how and where it can address critical healthcare challenges. We first describe the theoretical foundations and pivotal methods of meta-learning. We then divide the employed meta-learning approaches in the healthcare domain into two main categories of multi/single-task learning and many/few-shot learning and survey the studies. Finally, we highlight the current challenges in meta-learning research, discuss the potential solutions, and provide future perspectives on meta-learning in healthcare.