# Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray

arXiv: https://arxiv.org/abs/2311.17334 · HTML: https://mlsanity.com/paper/2311.17334/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [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)
- [Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets](https://mlsanity.com/paper/2007.09654/) (2020)
- [Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study](https://mlsanity.com/paper/2208.13365/) (2022)
- [Large Loss Matters in Weakly Supervised Multi-Label Classification](https://mlsanity.com/paper/2206.03740/) (2022)
- [Multi-label Thoracic Disease Image Classification with Cross-Attention Networks](https://mlsanity.com/paper/2007.10859/) (2020)
- [Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification](https://mlsanity.com/paper/2005.10550/) (2020)
- [NVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification](https://mlsanity.com/paper/2103.04053/) (2021)

## Built on by (most cited first, 1 of 1)

- [TRCGL-Net: A Long-Tailed Multi-Label Chest X-Ray Classification Framework with Generative Data Augmentation and Label Co-Occurrence Modeling](https://mlsanity.com/paper/2607.00975/) (2026)

## Abstract (arXiv)

Chest X-rays (CXR) often reveal rare diseases, demanding precise diagnosis. However, current computer-aided diagnosis (CAD) methods focus on common diseases, leading to inadequate detection of rare conditions due to the absence of comprehensive datasets. To overcome this, we present a novel benchmark for long-tailed multi-label classification in CXRs, encapsulating both common and rare thoracic diseases. Our approach includes developing the "LTML-MIMIC-CXR" dataset, an augmentation of MIMIC-CXR with 26 additional rare diseases. We propose a baseline method for this classification challenge, integrating adaptive negative regularization to address negative logits' over-suppression in tail classes, and a large loss reconsideration strategy for correcting noisy labels from automated annotations. Our evaluation on LTML-MIMIC-CXR demonstrates significant advancements in rare disease detection. This work establishes a foundation for robust CAD methods, achieving a balance in identifying a spectrum of thoracic diseases in CXRs. Access to our code and dataset is provided at:https://github.com/laihaoran/LTML-MIMIC-CXR.