Deep learning methods for chest X-ray interpretation typically rely on pretrained models developed for ImageNet. This paradigm assumes that better ImageNet architectures perform better on chest X-ray tasks and that ImageNet-pretrained weights provide a performance boost over random initialization. In this work, we compare the transfer performance and parameter efficiency of 16 popular convolutional architectures on a large chest X-ray dataset (CheXpert) to investigate these assumptions. First, we find no relationship between ImageNet performance and CheXpert performance for both models without pretraining and models with pretraining. Second, we find that, for models without pretraining, the choice of model family influences performance more than size within a family for medical imaging tasks. Third, we observe that ImageNet pretraining yields a statistically significant boost in performance across architectures, with a higher boost for smaller architectures. Fourth, we examine whether ImageNet architectures are unnecessarily large for CheXpert by truncating final blocks from pretrained models, and find that we can make models 3.25x more parameter-efficient on average without a statistically significant drop in performance. Our work contributes new experimental evidence about the relation of ImageNet to chest x-ray interpretation performance.

 本文研究了预训练模型在胸透解释方面的应用，通过对16种不同的卷积神经网络评估它们的迁移学习效果和参数效率，发现ImageNet性能和CheXpert性能之间不存在关系，而ImageNet预训练可以提高胸透解释的性能，并通过截取最后一层的方法使模型可节省3.25倍的参数而不影响性能。

CheXtransfer: 使用ImageNet模型进行胸部X射线解读的性能和参数效率