A review of transfer learning for medical image classification

Abstract

This review paper provides an overview of the peer-reviewed articles using transfer learning for medical image analysis, while also providing guidelines for selecting a convolutional neural network model and its configurations for the image classification task. The data characteristics and the trend of models and transfer learning types in the medical domain are additionally analyzed. Publications were retrieved from the databases PubMed and Web of Science of peer-reviewed articles published in English until December 31, 2020. We followed the PRISMA guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this review. With respect to the model, the majority of studies (n = 57) empirically evaluated numerous models followed by deep (n = 33) and shallow (n = 24) models. With respect to the transfer learning approaches, the majority of studies (n = 46) empirically searched for the optimal transfer learning configuration followed by feature extractor (n = 38) and fine-tuning scratch (n = 27), feature extractor hybrid (n = 7) and fine-tuning (n = 3). The investigated studies showed that transfer learning demonstrates either a better or at least a similar performance compared to medical experts despite the limited data sets. We hence encourage data scientists and practitioners to use models such as ResNet or Inception with a feature extractor approach, which saves computational costs and time without degrading the predictive power.

Deep Learning for Contrast Enhanced Mammography - a Systematic Review

“…With this approach, a model developed for a specific task is reused as the starting point for a model applied for a different task. It transfers knowledge from the previously learned context to the new application, potentially enabling to achieve better performance with less data (Figure 6) (20).…”