mlsanityData to 7 Oct 2026

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

arXiv 2007.07423 ↗First posted 15 Jul 2020cs.CV cs.LGcited by 69

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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.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jul 2020.

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Citing papers per year: 2021: 16; 2022: 22; 2023: 11; 2024: 8; 2025: 9; 2026: 3.

69 papers.

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  1. 2021Big Self-Supervised Models Advance Medical Image Classificationcited by 320
  2. 2023Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Imagescited by 128
  3. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  4. 2021Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reportscited by 80
  5. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  6. 2023Advancing Radiograph Representation Learning with Masked Record Modelingcited by 75
  7. 2022Masked Image Modeling Advances 3D Medical Image Analysiscited by 71
  8. 2023Large AI Models in Health Informatics: Applications, Challenges, and the Futurecited by 61
  9. 2021Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contextscited by 59
  10. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  11. 2022DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysiscited by 48
  12. 2024FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Modelscited by 47
38 more
  1. 2023PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysiscited by 40
  2. 2023Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learningcited by 34
  3. 2021One-Shot Medical Landmark Detectioncited by 31
  4. 2022A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainabilitycited by 30
  5. 2025Scaling up self-supervised learning for improved surgical foundation modelscited by 25
  6. 2021SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbationcited by 19
  7. 2021Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learningcited by 19
  8. 2022CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imagingcited by 18
  9. 2025CheXWorld: Exploring Image World Modeling for Radiograph Representation Learningcited by 18
  10. 2021Which images to label for few-shot medical landmark detection?cited by 16
  11. 2021Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classificationcited by 14
  12. 2021Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applicationscited by 12
  13. 2021An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysiscited by 10
  14. 2023A Review of Predictive and Contrastive Self-supervised Learning for Medical Imagescited by 10
  15. 2026Efficient Chest X-ray Representation Learning via Semantic-Partitioned Contrastive Learningcited by 10
  16. 2022Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Imagescited by 9
  17. 2022FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Gradingcited by 9
  18. 2022Free Lunch for Surgical Video Understanding by Distilling Self-Supervisionscited by 8
  19. 2022Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasoundcited by 8
  20. 2021Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlationcited by 6
  21. 2021Improving Tail-Class Representation with Centroid Contrastive Learningcited by 6
  22. 2024Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domaincited by 6
  23. 2022Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagationcited by 5
  24. 2022Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learningcited by 5
  25. 2024Enhancing the vision-language foundation model with key semantic knowledge-emphasized report refinementcited by 5
  26. 2025Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Modelscited by 5
  27. 2021MixSearch: Searching for Domain Generalized Medical Image Segmentation Architecturescited by 4
  28. 2022Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image Analysiscited by 4
  29. 2023Learning Better Contrastive View from Radiologist's Gazecited by 4
  30. 2021Unsupervised Local Discrimination for Medical Imagescited by 3
  31. 2022Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-trainingcited by 3
  32. 2022MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imagingcited by 3
  33. 2023A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labelscited by 3
  34. 2024From Barlow Twins to Triplet Training: Differentiating Dementia with Limited Datacited by 3
  35. 2025AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic Imagescited by 3
  36. 2021Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rayscited by 2
  37. 2022Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesionscited by 2
  38. 2023A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Imagescited by 2

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