MedMAE：面向医学影像任务的自监督骨干网络
MedMAE: A Self-Supervised Backbone for Medical Imaging Tasks
AI总结：
针对医学影像标注数据稀缺及域偏移问题，提出大规模无标注数据集及基于掩码自编码器的自监督骨干网络MedMAE，在四项医学影像任务上优于现有预训练模型。
摘要：
医学影像任务因缺乏公开可用的标注数据集而极具挑战性。因此，现有深度学习模型难以实现高性能，因为它们需要大规模标注数据集才能有效训练。一种替代方案是使用预训练模型，并利用医学影像数据集对其进行微调。然而，所有现有模型均使用自然图像进行预训练，这与医学影像领域完全不同，导致因域偏移而产生较差的性能。为克服这些问题，我们提出了一个大规模无标注医学影像数据集，并利用所提出的数据集，采用一种称为掩码自编码器的自监督学习技术，预训练了一个骨干网络。该骨干网络可作为任何医学影像任务的预训练模型，因为它被训练用于学习不同类型医学影像的视觉表征。为评估所提骨干网络的性能，我们使用了四项不同的医学影像任务。将结果与现有预训练模型进行了比较。这些实验证明了我们提出的骨干网络在医学影像任务中的优越性。
英文摘要：
Medical imaging tasks are very challenging due to the lack of publicly available labeled datasets. Hence, it is difficult to achieve high performance with existing deep-learning models as they require a massive labeled dataset to be trained effectively. An alternative solution is to use pre-trained models and fine-tune them using the medical imaging dataset. However, all existing models are pre-trained using natural images, which is a completely different domain from that of medical imaging, which leads to poor performance due to domain shift. To overcome these problems, we propose a large-scale unlabeled dataset of medical images and a backbone pre-trained using the proposed dataset with a self-supervised learning technique called Masked autoencoder. This backbone can be used as a pre-trained model for any medical imaging task, as it is trained to learn a visual representation of different types of medical images. To evaluate the performance of the proposed backbone, we used four different medical imaging tasks. The results are compared with existing pre-trained models. These experiments show the superiority of our proposed backbone in medical imaging tasks.
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。
