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.

通过比较在医疗领域中的迁移学习与自监督学习的性能和鲁棒性，本文从相同的源域数据集采用不同的预训练方法分别预训练了两个模型，并在小规模医疗数据集上评估了它们，以识别影响其最终性能的因素。通过比较实验测试了医疗领域中的数据失衡、数据稀缺和数据领域不匹配等常见问题对特定的预训练模型的影响。最后，提供了帮助用户在医疗领域应用迁移学习和自监督学习方法，并构建更方便和高效的部署策略的建议。

迁移学习与自监督学习的实验比较