# How Can We Tame the Long-Tail of Chest X-ray Datasets?

arXiv: https://arxiv.org/abs/2309.04293 · HTML: https://mlsanity.com/paper/2309.04293/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 17 of 17)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [TorchXRayVision: A library of chest X-ray datasets and models](https://mlsanity.com/paper/2111.00595/) (2021)
- [Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets](https://mlsanity.com/paper/2007.09654/) (2020)
- [RoentGen: Vision-Language Foundation Model for Chest X-ray Generation](https://mlsanity.com/paper/2211.12737/) (2022)
- [Self-Supervised Pretraining Improves Self-Supervised Pretraining](https://mlsanity.com/paper/2103.12718/) (2021)
- [What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factors](https://mlsanity.com/paper/2203.01825/) (2022)
- [Balanced-MixUp for Highly Imbalanced Medical Image Classification](https://mlsanity.com/paper/2109.09850/) (2021)
- [Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study](https://mlsanity.com/paper/2208.13365/) (2022)
- [ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification](https://mlsanity.com/paper/2209.00183/) (2022)
- [Can we Adopt Self-supervised Pretraining for Chest X-Rays?](https://mlsanity.com/paper/2211.12931/) (2022)
- [BB-GCN: A Bi-modal Bridged Graph Convolutional Network for Multi-label Chest X-Ray Recognition](https://mlsanity.com/paper/2302.11082/) (2023)

## Built on by (most cited first, 2 of 2)

- [Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge](https://mlsanity.com/paper/2310.16112/) (2023)
- [Generalist-Specialist Mixture-of-Experts for Rare Pathology Detection in Multimodal Imaging](https://mlsanity.com/paper/2609.18688/) (2026)

## Abstract (arXiv)

Chest X-rays (CXRs) are a medical imaging modality that is used to infer a large number of abnormalities. While it is hard to define an exhaustive list of these abnormalities, which may co-occur on a chest X-ray, few of them are quite commonly observed and are abundantly represented in CXR datasets used to train deep learning models for automated inference. However, it is challenging for current models to learn independent discriminatory features for labels that are rare but may be of high significance. Prior works focus on the combination of multi-label and long tail problems by introducing novel loss functions or some mechanism of re-sampling or re-weighting the data. Instead, we propose that it is possible to achieve significant performance gains merely by choosing an initialization for a model that is closer to the domain of the target dataset. This method can complement the techniques proposed in existing literature, and can easily be scaled to new labels. Finally, we also examine the veracity of synthetically generated data to augment the tail labels and analyse its contribution to improving model performance.