Taxlifier: Leveraging Disease Taxonomy for Enhanced Multi-Label Classification in Chest Radiography
arXiv:2607.05628v1 Announce Type: cross 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 te
The proliferation of medical imaging data and advancements in AI/ML techniques for image recognition provide the impetus for developing more sophisticated diagnostic tools now.
Improved multi-label classification in medical imaging can lead to earlier, more accurate disease diagnosis, reducing diagnostic errors and improving patient outcomes.
The ability to leverage disease taxonomy for hierarchical classification refines previous flat classification models, offering a more nuanced and potentially more accurate interpretation of complex medical images.
- · Healthcare sector
- · Medical AI developers
- · Patients
- · Diagnostic imaging centers
- · Traditional diagnostic methods (manual interpretation)
Physicians gain enhanced diagnostic support for complex thoracic conditions from AI tools.
Reduced misdiagnosis rates could lead to more effective early interventions and lower overall healthcare costs.
The success of this approach could accelerate the adoption of similar hierarchical AI models across other complex medical imaging or diagnostic domains.
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