迁移学习还是自监督学习？弥合医学影像中的性能差距
Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imaging
AI总结：
本文对比迁移学习与自监督学习在医学影像任务中的性能和鲁棒性，通过同源数据不同预训练方式及小规模医学数据集实验，分析数据不平衡、稀缺和域不匹配的影响并给出部署建议。
摘要：
近年来，迁移学习和自监督学习因其能够缓解数据有限带来的挑战、提升模型泛化能力并降低计算开销，在医学领域受到广泛关注。迁移学习和自监督学习在推动医学研究方面具有巨大潜力。然而，必须认识到，迁移学习和自监督学习架构具有不同的优势与局限，在准确率、训练速度和鲁棒性方面表现出差异。本文比较了迁移学习与自监督学习在医学领域中的性能和鲁棒性。具体而言，我们使用相同的源域数据集、采用不同的预训练方法预训练了两个模型，并在小规模医学数据集上进行评估，以识别影响其最终性能的因素。我们通过对比实验测试了医学领域中若干常见问题的数据，例如数据不平衡、数据稀缺和域不匹配，以理解它们对特定预训练模型的影响。最后，我们提供建议，帮助用户在医学领域应用迁移学习和自监督学习方法，并构建更便捷、高效的部署策略。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
