In deep learning era, pretrained models play an important role in medical
image analysis, in which ImageNet pretraining has been widely adopted as the
best way. However, it is undeniable that there exists an obvious domain gap
between natural images and medical images. To bridge this gap, we propose a new
pretraining method which learns from 700k radiographs given no manual
annotations. We call our method as Comparing to Learn (C2L) because it learns
robust features by comparing different image representations. To verify the
effectiveness of C2L, we conduct comprehensive ablation studies and evaluate it
on different tasks and datasets. The experimental results on radiographs show
that C2L can outperform ImageNet pretraining and previous state-of-the-art
approaches significantly. Code and models are available.

本文提出了一种名为 Comparing to Learn（C2L）的预训练方法，通过比较不同的图像表示来学习稳健特征，在放射线方面的实验结果显示，C2L 在医学图像分析领域中能够显著优于 ImageNet 预训练和以往的最先进方法。