# Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study

arXiv: https://arxiv.org/abs/2208.13365 · HTML: https://mlsanity.com/paper/2208.13365/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 29 Aug 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [Long-tailed Recognition by Routing Diverse Distribution-Aware Experts](https://mlsanity.com/paper/2010.01809/) (2020)
- [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)
- [A deep learning system for differential diagnosis of skin diseases](https://mlsanity.com/paper/1909.05382/) (2019)
- [Influence-Balanced Loss for Imbalanced Visual Classification](https://mlsanity.com/paper/2110.02444/) (2021)
- [NegBio: a high-performance tool for negation and uncertainty detection in radiology reports](https://mlsanity.com/paper/1712.05898/) (2017)
- [Label-Imbalanced and Group-Sensitive Classification under Overparameterization](https://mlsanity.com/paper/2103.01550/) (2021)
- [Self-Damaging Contrastive Learning](https://mlsanity.com/paper/2106.02990/) (2021)
- [Balanced-MixUp for Highly Imbalanced Medical Image Classification](https://mlsanity.com/paper/2109.09850/) (2021)
- [Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition](https://mlsanity.com/paper/2104.11057/) (2021)
- [Difficulty-aware Meta-learning for Rare Disease Diagnosis](https://mlsanity.com/paper/1907.00354/) (2019)
- [Automatic detection of rare pathologies in fundus photographs using few-shot learning](https://mlsanity.com/paper/1907.09449/) (2019)

## Built on by (most cited first, 30 of 45)

- [Class-Balancing Diffusion Models](https://mlsanity.com/paper/2305.00562/) (2023)
- [Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge](https://mlsanity.com/paper/2310.16112/) (2023)
- [Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research](https://mlsanity.com/paper/2311.09402/) (2023)
- [A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models](https://mlsanity.com/paper/2402.11217/) (2024)
- [Latent-based Diffusion Model for Long-tailed Recognition](https://mlsanity.com/paper/2404.04517/) (2024)
- [CheXFusion: Effective Fusion of Multi-View Features using Transformers for Long-Tailed Chest X-Ray Classification](https://mlsanity.com/paper/2308.03968/) (2023)
- [FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition](https://mlsanity.com/paper/2305.17421/) (2023)
- [CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray](https://mlsanity.com/paper/2506.07984/) (2025)
- [Full Conformal Adaptation of Medical Vision-Language Models](https://mlsanity.com/paper/2506.06076/) (2025)
- [Robust Asymmetric Loss for Multi-Label Long-Tailed Learning](https://mlsanity.com/paper/2308.05542/) (2023)
- [Trustworthy Few-Shot Transfer of Medical VLMs through Split Conformal Prediction](https://mlsanity.com/paper/2506.17503/) (2025)
- [Few-Shot, Now for Real: Medical VLMs Adaptation without Balanced Sets or Validation](https://mlsanity.com/paper/2506.17500/) (2025)
- [MAGDA: Multi-agent guideline-driven diagnostic assistance](https://mlsanity.com/paper/2409.06351/) (2024)
- [A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation](https://mlsanity.com/paper/2604.00493/) (2026)
- [Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification](https://mlsanity.com/paper/2307.14959/) (2023)
- [RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Models](https://mlsanity.com/paper/2410.00307/) (2024)
- [LCMem: A Universal Model for Robust Image Memorization Detection](https://mlsanity.com/paper/2512.14421/) (2025)
- [MONICA: Benchmarking on Long-tailed Medical Image Classification](https://mlsanity.com/paper/2410.02010/) (2024)
- [Causal Disentanglement for Robust Long-tail Medical Image Generation](https://mlsanity.com/paper/2504.14450/) (2025)
- [Overview of the CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification](https://mlsanity.com/paper/2602.22092/) (2026)
- [How Can We Tame the Long-Tail of Chest X-ray Datasets?](https://mlsanity.com/paper/2309.04293/) (2023)
- [A Reality Check of Vision-Language Pre-training in Radiology: Have We Progressed Using Text?](https://mlsanity.com/paper/2504.05227/) (2025)
- [GRASP: Guided Residual Adapters with Sample-wise Partitioning](https://mlsanity.com/paper/2512.01675/) (2025)
- [LATA: Laplacian-Assisted Transductive Adaptation for Conformal Uncertainty in Medical VLMs](https://mlsanity.com/paper/2602.17535/) (2026)
- [Bag of Tricks for Long-Tailed Multi-Label Classification on Chest X-Rays](https://mlsanity.com/paper/2308.08853/) (2023)
- [Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays](https://mlsanity.com/paper/2309.04462/) (2023)
- [Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray](https://mlsanity.com/paper/2311.17334/) (2023)
- [LTCXNet: Advancing Chest X-Ray Analysis with Solutions for Long-Tailed Multi-Label Classification and Fairness Challenges](https://mlsanity.com/paper/2411.10746/) (2024)
- [Understanding Dataset Bias in Medical Imaging: A Case Study on Chest X-rays](https://mlsanity.com/paper/2507.07722/) (2025)
- [CXR-CML: Improved zero-shot classification of long-tailed multi-label diseases in Chest X-Rays](https://mlsanity.com/paper/2507.19398/) (2025)

## Abstract (arXiv)

Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings. While a trained radiologist can learn the visual presentation of rare conditions by studying a few representative examples, teaching a machine to learn from such a "long-tailed" distribution is much more difficult, as standard methods would be easily biased toward the most frequent classes. In this paper, we present a comprehensive benchmark study of the long-tailed learning problem in the specific domain of thorax diseases on chest X-rays. We focus on learning from naturally distributed chest X-ray data, optimizing classification accuracy over not only the common "head" classes, but also the rare yet critical "tail" classes. To accomplish this, we introduce a challenging new long-tailed chest X-ray benchmark to facilitate research on developing long-tailed learning methods for medical image classification. The benchmark consists of two chest X-ray datasets for 19- and 20-way thorax disease classification, containing classes with as many as 53,000 and as few as 7 labeled training images. We evaluate both standard and state-of-the-art long-tailed learning methods on this new benchmark, analyzing which aspects of these methods are most beneficial for long-tailed medical image classification and summarizing insights for future algorithm design. The datasets, trained models, and code are available at https://github.com/VITA-Group/LongTailCXR.