arXiv:2606.11915v1 Announce Type: cross Abstract: We present a quality-adaptive angular-margin learning framework that improves feature generalization by enforcing intra-class compactness and inter-class separability. Our framework, titled QLung, introduces a no-reference audio quality margin derived from spectral entropy and root-mean-square energy, which adaptively scales angular margins based on recording quality. To this end, we propose a log-scaled angular margin that stabilizes training under severe class imbalance. We also use an angular classifier that normalizes features and class wei
The paper presents a new machine learning framework for respiratory sound classification, indicating ongoing incremental advancements in AI applications for medical diagnostics.
This development could improve the accuracy and robustness of AI systems for health monitoring, particularly in diagnosing respiratory conditions using sound data.
The proposed QLung framework offers a method to handle varying audio quality and class imbalance, potentially leading to more reliable AI models in real-world clinical settings.
- · Medical diagnostics companies
- · Healthcare AI developers
- · Patients with respiratory conditions
Improved early detection of respiratory diseases through better AI models.
Increased adoption of AI-powered diagnostic tools in telemedicine and clinical practice.
Reduced burden on healthcare systems through automated or semi-automated screening processes for pulmonary health.
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Read at arXiv cs.AI