# Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

arXiv: https://arxiv.org/abs/1803.02315 · HTML: https://mlsanity.com/paper/1803.02315/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

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

- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification](https://mlsanity.com/paper/2210.12843/) (2022)
- [A comprehensive survey on deep active learning in medical image analysis](https://mlsanity.com/paper/2310.14230/) (2023)
- [Review of multimodal machine learning approaches in healthcare](https://mlsanity.com/paper/2402.02460/) (2024)
- [GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification](https://mlsanity.com/paper/2305.18221/) (2023)
- [Learning Hierarchical Attention for Weakly-supervised Chest X-Ray Abnormality Localization and Diagnosis](https://mlsanity.com/paper/2112.12349/) (2021)
- [Label-Assemble: Leveraging Multiple Datasets with Partial Labels](https://mlsanity.com/paper/2109.12265/) (2021)
- [Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research](https://mlsanity.com/paper/2311.09402/) (2023)
- [Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack](https://mlsanity.com/paper/2103.03438/) (2021)
- [SwinCheX: Multi-label classification on chest X-ray images with transformers](https://mlsanity.com/paper/2206.04246/) (2022)
- [Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers](https://mlsanity.com/paper/2202.06076/) (2022)
- [Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data](https://mlsanity.com/paper/2205.03168/) (2022)
- [Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.06486/) (2021)
- [Nodule detection and generation on chest X-rays: NODE21 Challenge](https://mlsanity.com/paper/2401.02192/) (2024)
- [Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice](https://mlsanity.com/paper/2102.01979/) (2021)
- [Semantic Representation and Dependency Learning for Multi-Label Image Recognition](https://mlsanity.com/paper/2204.03795/) (2022)
- [HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images](https://mlsanity.com/paper/2310.06143/) (2023)
- [Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations](https://mlsanity.com/paper/2101.07945/) (2021)
- [ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification](https://mlsanity.com/paper/2210.02998/) (2022)
- [What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification](https://mlsanity.com/paper/2202.03714/) (2022)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [Learning Low-Rank Feature for Thorax Disease Classification](https://mlsanity.com/paper/2404.18933/) (2024)
- [CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding](https://mlsanity.com/paper/2509.23379/) (2025)
- [Multi-label Ranking: Mining Multi-label and Label Ranking Data](https://mlsanity.com/paper/2101.00583/) (2021)
- [Rheumatoid Arthritis: Automated Scoring of Radiographic Joint Damage](https://mlsanity.com/paper/2110.08812/) (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)
- [Searching for Pneumothorax in X-Ray Images Using Autoencoded Deep Features](https://mlsanity.com/paper/2102.06096/) (2021)
- [Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification](https://mlsanity.com/paper/2102.13269/) (2021)
- [Multi-target prediction for dummies using two-branch neural networks](https://mlsanity.com/paper/2104.09967/) (2021)

## Abstract (arXiv)

The increased availability of X-ray image archives (e.g. the ChestX-ray14 dataset from the NIH Clinical Center) has triggered a growing interest in deep learning techniques. To provide better insight into the different approaches, and their applications to chest X-ray classification, we investigate a powerful network architecture in detail: the ResNet-50. Building on prior work in this domain, we consider transfer learning with and without fine-tuning as well as the training of a dedicated X-ray network from scratch. To leverage the high spatial resolution of X-ray data, we also include an extended ResNet-50 architecture, and a network integrating non-image data (patient age, gender and acquisition type) in the classification process. In a concluding experiment, we also investigate multiple ResNet depths (i.e. ResNet-38 and ResNet-101). In a systematic evaluation, using 5-fold re-sampling and a multi-label loss function, we compare the performance of the different approaches for pathology classification by ROC statistics and analyze differences between the classifiers using rank correlation. Overall, we observe a considerable spread in the achieved performance and conclude that the X-ray-specific ResNet-38, integrating non-image data yields the best overall results. Furthermore, class activation maps are used to understand the classification process, and a detailed analysis of the impact of non-image features is provided.