# Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations

arXiv: https://arxiv.org/abs/2007.07423 · HTML: https://mlsanity.com/paper/2007.07423/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 30 of 69)

- [Big Self-Supervised Models Advance Medical Image Classification](https://mlsanity.com/paper/2101.05224/) (2021)
- [Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images](https://mlsanity.com/paper/2302.14042/) (2023)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports](https://mlsanity.com/paper/2111.03452/) (2021)
- [Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions](https://mlsanity.com/paper/2404.03264/) (2024)
- [Advancing Radiograph Representation Learning with Masked Record Modeling](https://mlsanity.com/paper/2301.13155/) (2023)
- [Masked Image Modeling Advances 3D Medical Image Analysis](https://mlsanity.com/paper/2204.11716/) (2022)
- [Large AI Models in Health Informatics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2303.11568/) (2023)
- [Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts](https://mlsanity.com/paper/2109.04379/) (2021)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis](https://mlsanity.com/paper/2204.10437/) (2022)
- [FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models](https://mlsanity.com/paper/2407.00983/) (2024)
- [PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis](https://mlsanity.com/paper/2301.00772/) (2023)
- [Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning](https://mlsanity.com/paper/2309.05904/) (2023)
- [One-Shot Medical Landmark Detection](https://mlsanity.com/paper/2103.04527/) (2021)
- [A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability](https://mlsanity.com/paper/2212.10888/) (2022)
- [Scaling up self-supervised learning for improved surgical foundation models](https://mlsanity.com/paper/2501.09436/) (2025)
- [SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation](https://mlsanity.com/paper/2106.01544/) (2021)
- [Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning](https://mlsanity.com/paper/2111.10827/) (2021)
- [CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning](https://mlsanity.com/paper/2504.13820/) (2025)
- [CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging](https://mlsanity.com/paper/2204.07344/) (2022)
- [Which images to label for few-shot medical landmark detection?](https://mlsanity.com/paper/2112.04386/) (2021)
- [Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification](https://mlsanity.com/paper/2103.03629/) (2021)
- [Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications](https://mlsanity.com/paper/2112.05760/) (2021)
- [An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis](https://mlsanity.com/paper/2106.09229/) (2021)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [Efficient Chest X-ray Representation Learning via Semantic-Partitioned Contrastive Learning](https://mlsanity.com/paper/2603.07113/) (2026)
- [Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images](https://mlsanity.com/paper/2203.07707/) (2022)
- [FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading](https://mlsanity.com/paper/2205.01676/) (2022)
- [Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions](https://mlsanity.com/paper/2205.09292/) (2022)

## Abstract (arXiv)

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.