Meta-learning in healthcare: A survey

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.

References in corpus (8)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
• A Survey of Deep Meta-Learning
• Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review
• Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels
• A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers
• Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation
• Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learning

Cited by in corpus (1)

• Robust Meta-Model for Predicting the Need for Blood Transfusion in Non-traumatic ICU Patients