mlsanityData to 7 Oct 2026

A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging

arXiv 1904.01638 ↗First posted 2 Apr 2019cs.CV cs.AI eess.IVcited by 11

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Abstract (arXiv)

This work provides a strong baseline for the problem of multi-source multi-target domain adaptation and generalization in medical imaging. Using a diverse collection of ten chest X-ray datasets, we empirically demonstrate the benefits of training medical imaging deep learning models on varied patient populations for generalization to out-of-sample domains.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Apr 2019.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 1; 2024: 3.

11 papers.

  1. 2021Model-Based Domain Generalizationcited by 103
  2. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  3. 2021Adversarial Robustness with Semi-Infinite Constrained Learningcited by 39
  4. 2023Progressive Random Convolutions for Single Domain Generalizationcited by 33
  5. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  6. 2024The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: an Experimental Studycited by 15
  7. 2021Few-Shot Domain Adaptation with Polymorphic Transformerscited by 14
  8. 2021Structured dataset documentation: a datasheet for CheXpertcited by 6
  9. 2022On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classificationcited by 5
  10. 2022On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practicescited by 3
  11. 2024Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populationscited by 0