# Chest X-rays Classification: A Multi-Label and Fine-Grained Problem

arXiv: https://arxiv.org/abs/1807.07247 · HTML: https://mlsanity.com/paper/1807.07247/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2018.

## Built on by (most cited first, 30 of 31)

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- [CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis](https://mlsanity.com/paper/2106.10230/) (2021)
- [Multi-layered Semantic Representation Network for Multi-label Image Classification](https://mlsanity.com/paper/2106.11596/) (2021)
- [HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images](https://mlsanity.com/paper/2310.06143/) (2023)
- [Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation](https://mlsanity.com/paper/2110.00404/) (2021)
- [Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification](https://mlsanity.com/paper/2206.13123/) (2022)
- [A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation](https://mlsanity.com/paper/2207.02410/) (2022)
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- [Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning](https://mlsanity.com/paper/2111.07646/) (2021)
- [Deep learning for understanding multilabel imbalanced Chest X-ray datasets](https://mlsanity.com/paper/2207.14408/) (2022)
- [Diffusion-Based Cross-Modal Feature Extraction for Multi-Label Classification](https://mlsanity.com/paper/2509.15553/) (2025)
- [Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification](https://mlsanity.com/paper/2204.01728/) (2022)
- [Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels](https://mlsanity.com/paper/2209.01314/) (2022)
- [Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System](https://mlsanity.com/paper/2109.12453/) (2021)
- [A novel adversarial learning strategy for medical image classification](https://mlsanity.com/paper/2206.11501/) (2022)
- [Improved Super Resolution of MR Images Using CNNs and Vision Transformers](https://mlsanity.com/paper/2207.11748/) (2022)
- [Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis](https://mlsanity.com/paper/2211.08424/) (2022)
- [LeDNet: Localization-enabled Deep Neural Network for Multi-Label Radiography Image Classification](https://mlsanity.com/paper/2407.03931/) (2024)
- [Time Frequency Analysis of EMG Signal for Gesture Recognition using Fine grained Features](https://mlsanity.com/paper/2504.14708/) (2025)
- [Semantic-Aware Representation Learning via Conditional Transport for Multi-Label Image Classification](https://mlsanity.com/paper/2507.14918/) (2025)
- [Incomplete Multi-Label Image Recognition by Co-learning Semantic-Aware Features and Label Recovery](https://mlsanity.com/paper/2510.10055/) (2025)
- [Bridging the Reproducibility Divide: Open Source Software's Role in Standardizing Healthcare AI](https://mlsanity.com/paper/2603.03367/) (2026)

## Abstract (arXiv)

The widely used ChestX-ray14 dataset addresses an important medical image classification problem and has the following caveats: 1) many lung pathologies are visually similar, 2) a variant of diseases including lung cancer, tuberculosis, and pneumonia are present in a single scan, i.e. multiple labels and 3) The incidence of healthy images is much larger than diseased samples, creating imbalanced data. These properties are common in medical domain. Existing literature uses stateof- the-art DensetNet/Resnet models being transfer learned where output neurons of the networks are trained for individual diseases to cater for multiple diseases labels in each image. However, most of them don't consider relationship between multiple classes. In this work we have proposed a novel error function, Multi-label Softmax Loss (MSML), to specifically address the properties of multiple labels and imbalanced data. Moreover, we have designed deep network architecture based on fine-grained classification concept that incorporates MSML. We have evaluated our proposed method on various network backbones and showed consistent performance improvements of AUC-ROC scores on the ChestX-ray14 dataset. The proposed error function provides a new method to gain improved performance across wider medical datasets.