TRCGL-Net: A Long-Tailed Multi-Label Chest X-Ray Classification Framework with Generative Data Augmentation and Label Co-Occurrence Modeling
arXiv:2607.00975v1 Announce Type: cross Abstract: Chest X-ray multi-label classification is a core task in intelligent medical imaging diagnosis. However, real clinical data often exhibit extreme long-tailed distributions, leading to degraded performance on rare diseases in tail classes. This issue is not only driven by data scarcity but also by two intrinsic factors:1) attenuation of tail-class lesion representations under complex anatomical backgrounds, and 2) dominance of head classes in modeling label co-occurrence relationships. To address these challenges, we propose TRCGL-Net. First, a
Advances in AI, particularly generative models, are enabling more sophisticated solutions to long-standing challenges in medical imaging analysis, specifically addressing data scarcity and imbalanced datasets.
Improving diagnostic accuracy for rare diseases in medical imaging can significantly impact patient outcomes and healthcare efficiency, while also demonstrating the increasing sophistication of AI applications in specialized fields.
This research introduces a framework that directly tackles the performance degradation of AI models on rare diseases, suggesting a path towards more equitable diagnostic capabilities across all patient conditions.
- · Medical AI developers
- · Healthcare providers
- · Patients with rare conditions
- · Diagnostic imaging companies
- · Traditional diagnostic methods
- · AI models without advanced augmentation
Increased accuracy in chest X-ray diagnoses, especially for less common pathologies.
Faster and more reliable identification of critical medical conditions, leading to earlier interventions and better treatment efficacy.
Potential for integration into autonomous diagnostic systems, reducing reliance on human radiological expertise in certain contexts.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI