# Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations Source: [Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:28:00.017Z Authors: Hong-Yu Zhou, Shuang Yu, Cheng Bian, Yifan Hu, Kai Ma, Yefeng Zheng Published: 2020-07-15T01:14:34.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on NIH ChestX-ray](): Image Classification; NIH ChestX-ray; Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()