# Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification

arXiv: https://arxiv.org/abs/1909.01940 · HTML: https://mlsanity.com/paper/1909.01940/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

## Built on by (most cited first, 30 of 57)

- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations](https://mlsanity.com/paper/2107.00520/) (2021)
- [How I failed machine learning in medical imaging -- shortcomings and recommendations](https://mlsanity.com/paper/2103.10292/) (2021)
- [An Empirical Framework for Domain Generalization in Clinical Settings](https://mlsanity.com/paper/2103.11163/) (2021)
- [Block Selection Method for Using Feature Norm in Out-of-distribution Detection](https://mlsanity.com/paper/2212.02295/) (2022)
- [Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity](https://mlsanity.com/paper/2102.04635/) (2021)
- [Detecting Shortcuts in Medical Images -- A Case Study in Chest X-rays](https://mlsanity.com/paper/2211.04279/) (2022)
- [When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations](https://mlsanity.com/paper/2308.04431/) (2023)
- [The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review](https://mlsanity.com/paper/2402.13635/) (2024)
- [DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray Scans](https://mlsanity.com/paper/2108.06490/) (2021)
- [Probabilistic Integration of Object Level Annotations in Chest X-ray Classification](https://mlsanity.com/paper/2210.06980/) (2022)
- [X-TRA: Improving Chest X-ray Tasks with Cross-Modal Retrieval Augmentation](https://mlsanity.com/paper/2302.11352/) (2023)
- [DomainATM: Domain Adaptation Toolbox for Medical Data Analysis](https://mlsanity.com/paper/2209.11890/) (2022)
- [A Systematic Review of Generalization Research in Medical Image Classification](https://mlsanity.com/paper/2403.12167/) (2024)
- [Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice](https://mlsanity.com/paper/2102.01979/) (2021)
- [Structured dataset documentation: a datasheet for CheXpert](https://mlsanity.com/paper/2105.03020/) (2021)
- [Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift](https://mlsanity.com/paper/2112.09196/) (2021)
- [Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacy](https://mlsanity.com/paper/2306.06503/) (2023)
- [DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data](https://mlsanity.com/paper/2308.03295/) (2023)
- [Medical Image Segmentation with Domain Adaptation: A Survey](https://mlsanity.com/paper/2311.01702/) (2023)
- [FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging](https://mlsanity.com/paper/2407.08822/) (2024)
- [EOOD: Entropy-based Out-of-distribution Detection](https://mlsanity.com/paper/2504.03342/) (2025)
- [Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic Systems](https://mlsanity.com/paper/2408.07828/) (2024)
- [CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays](https://mlsanity.com/paper/2103.04590/) (2021)
- [Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation](https://mlsanity.com/paper/2105.07715/) (2021)
- [Deep learning for understanding multilabel imbalanced Chest X-ray datasets](https://mlsanity.com/paper/2207.14408/) (2022)
- [On Evaluating Adversarial Robustness of Chest X-ray Classification: Pitfalls and Best Practices](https://mlsanity.com/paper/2212.08130/) (2022)
- [Preserving Fairness in AI under Domain Shift](https://mlsanity.com/paper/2301.12369/) (2023)
- [One Wave To Explain Them All: A Unifying Perspective On Feature Attribution](https://mlsanity.com/paper/2410.01482/) (2024)
- [AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift](https://mlsanity.com/paper/2507.07820/) (2025)

## Abstract (arXiv)

While deep learning models become more widespread, their ability to handle unseen data and generalize for any scenario is yet to be challenged. In medical imaging, there is a high heterogeneity of distributions among images based on the equipment that generates them and their parametrization. This heterogeneity triggers a common issue in machine learning called domain shift, which represents the difference between the training data distribution and the distribution of where a model is employed. A high domain shift tends to implicate in a poor generalization performance from the models. In this work, we evaluate the extent of domain shift on four of the largest datasets of chest radiographs. We show how training and testing with different datasets (e.g., training in ChestX-ray14 and testing in CheXpert) drastically affects model performance, posing a big question over the reliability of deep learning models trained on public datasets. We also show that models trained on CheXpert and MIMIC-CXR generalize better to other datasets.