PulmoSight-XAI: An Explainable Multi-View Attention Ensemble with Gradient Boosting Meta-Learning for Multi-Label Chest X-Ray Classification
arXiv:2607.04478v1 Announce Type: cross Abstract: Automated chest X-ray classification remains challenging due to severe class imbalance, co-occurring pathologies, and the loss of localized features in conventional architectures. To address these, we propose an explainable hierarchical multi-view ensemble framework for the robust classification of 14 thoracic pathologies. The framework employs view-specific training by independently modeling frontal and lateral radiographs using an ensemble of five complementary convolutional neural networks. Replacing global average pooling, a multi-scale fea
The proliferation of advanced AI techniques and increasing computational power allows for sophisticated medical imaging analysis, addressing long-standing challenges in automated diagnostics.
This development improves diagnostic accuracy and explainability in critical medical fields like radiology, directly impacting patient outcomes and healthcare efficiency.
The ability to more reliably and interpretably classify complex medical conditions from imaging data, potentially leading to faster and more accurate diagnoses in clinical settings.
- · Healthcare sector
- · Medical AI researchers
- · Patients with thoracic pathologies
- · Diagnostic imaging companies
- · Traditional manual diagnostic workflows
Improved early detection rates for various thoracic pathologies.
Increased adoption of AI-driven diagnostic tools in hospitals and clinics worldwide.
Reduced burden on human radiologists, allowing them to focus on more complex cases while AI handles routine screening.
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Read at arXiv cs.AI