# A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging

arXiv: https://arxiv.org/abs/1904.01638 · HTML: https://mlsanity.com/paper/1904.01638/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 11 of 11)

- [Model-Based Domain Generalization](https://mlsanity.com/paper/2102.11436/) (2021)
- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Adversarial Robustness with Semi-Infinite Constrained Learning](https://mlsanity.com/paper/2110.15767/) (2021)
- [Progressive Random Convolutions for Single Domain Generalization](https://mlsanity.com/paper/2304.00424/) (2023)
- [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024)
- [The Impact of Scanner Domain Shift on Deep Learning Performance in Medical Imaging: an Experimental Study](https://mlsanity.com/paper/2409.04368/) (2024)
- [Few-Shot Domain Adaptation with Polymorphic Transformers](https://mlsanity.com/paper/2107.04805/) (2021)
- [Structured dataset documentation: a datasheet for CheXpert](https://mlsanity.com/paper/2105.03020/) (2021)
- [On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification](https://mlsanity.com/paper/2205.00895/) (2022)
- [On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices](https://mlsanity.com/paper/2212.08130/) (2022)
- [Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations](https://mlsanity.com/paper/2402.05713/) (2024)

## 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.