  Recently, transfer learning and self-supervised learning have gained
significant attention within the medical field due to their ability to mitigate
the challenges posed by limited data availability, improve model
generalisation, and reduce computational expenses. Transfer learning and
self-supervised learning hold immense potential for advancing medical research.
However, it is crucial to recognise that transfer learning and self-supervised
learning architectures exhibit distinct advantages and limitations, manifesting
variations in accuracy, training speed, and robustness. This paper compares the
performance and robustness of transfer learning and self-supervised learning in
the medical field. Specifically, we pre-trained two models using the same
source domain datasets with different pre-training methods and evaluated them
on small-sized medical datasets to identify the factors influencing their final
performance. We tested data with several common issues in medical domains, such
as data imbalance, data scarcity, and domain mismatch, through comparison
experiments to understand their impact on specific pre-trained models. Finally,
we provide recommendations to help users apply transfer learning and
self-supervised learning methods in medical areas, and build more convenient
and efficient deployment strategies.
