Taxlifier: Leveraging Disease Taxonomy for Enhanced Multi-Label Classification in Chest Radiography

2026-07-06

AI Summary

This study addresses the challenge of multi-label chest X-ray classification, where co-occurring diseases exhibit highly overlapping visual features, limiting model performance. To tackle this issue, the authors propose a novel deep learning framework that explicitly incorporates the hierarchical structure of a chest disease ontology into multi-label classification for the first time. Two hierarchical strategies—loss-based and logit-based—are introduced to model semantic dependencies among diseases. The approach not only enhances model interpretability and clinical relevance but also achieves state-of-the-art results across three major benchmarks: CheXpert, PADCHEST, and NIH. Significant improvements are observed, with gains of up to 12% in accuracy, 13% in AUC, and 24% in F1 score compared to existing methods.

Abstract

Accurate and efficient classification of thoracic diseases in chest X-ray (CXR) images is crucial for timely diagnosis and treatment. However, the presence of multiple pathologies with overlapping visual characteristics poses significant challenges for automated classification systems. In this study, we propose two novel hierarchical multi-label classification techniques, namely the loss-based and logit-based methods, to address these challenges by leveraging the hierarchical relationships among different thoracic pathologies. The loss-based technique integrates hierarchical information directly into the optimization process, while the logit-based method adjusts the predicted probabilities of each class based on its parent class in the disease taxonomy. We evaluate the performance of both techniques using three large-scale CXR datasets: CheXpert (224,316 CXRs), PADCHEST (160,000 CXRs), and NIH (112,120 CXRs). The experimental results demonstrate significant improvements in accuracy, AUC, and F1 scores compared to the baseline method across various pathologies. The logit-based and loss-based methods improve accuracy by 12\% and 11\%, AUC by 13\% and 10\%, and F1 scores by 24\% and 12\%, respectively compared to the baseline. These results represent a substantial improvement over the baseline method. Furthermore, we conduct a comprehensive statistical analysis to validate the robustness and reliability of the proposed techniques. The integration of domain-specific hierarchical knowledge not only enhances the classification performance but also provides a more interpretable output for clinical decision support. Our findings highlight the potential of hierarchical multi-label classification in advancing computer-aided diagnosis systems for chest radiography.

Problem

• multi-label classification
• chest radiography
• thoracic diseases
• disease taxonomy
• overlapping visual characteristics

Innovation

• hierarchical multi-label classification
• disease taxonomy
• chest radiography
• loss-based method
• logit-based method