TRCGL-Net: A Long-Tailed Multi-Label Chest X-Ray Classification Framework with Generative Data Augmentation and Label Co-Occurrence Modeling

2026-07-01

AI Summary

This work addresses the performance degradation in rare disease recognition caused by extreme long-tailed distributions in multi-label chest X-ray classification. To mitigate this challenge, the authors propose a novel framework that synergistically integrates text-guided generation with structured modeling. Specifically, a text-conditioned diffusion model synthesizes semantically consistent samples for tail classes, while channel re-weighting and a class-aware attention mechanism enhance lesion-related features. Furthermore, a label co-occurrence–based graph convolutional network facilitates inter-class information propagation. This approach represents the first unified integration of generative augmentation, feature recalibration, and graph-structured modeling to effectively alleviate class imbalance. Evaluated on the PadChest dataset, the method achieves a mean average precision (mAP) of 0.4904 on tail classes, an overall mAP of 0.4408, and a mean area under the ROC curve (mAUC) of 0.8989, outperforming current state-of-the-art methods.

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 learnable text-guided conditional diffusion model is employed to generate high-quality tail-class chest X-ray image samples under disease semantic constraints, improving data diversity and realism of rare disease patterns while alleviating class imbalance and preserving pathology-consistent semantics.Second, a channel reweighting mechanism is introduced to perform feature recalibration by emphasizing disease-relevant feature channels, thereby improving feature discriminability under long-tailed distributions.A class-aware attention mechanism is further applied to generate class-specific attention maps, enabling the model to localize disease-relevant regions and focus on fine-grained lesion areas.Finally, a graph convolution network based on label co occurrence is introduced to establish an information propagation mechanism among categories. Experiments on the PadChest dataset show that the proposed method achieves a tail-class mAP of 0.4904, an overall mAP of 0.4408, and an mAUC of 0.8989, outperforming state-of-the-art methods. TRCGL-Net effectively improves recognition performance for rare diseases under long-tailed distributions and mitigates the impact of extreme class imbalance in chest X-ray multi-label classification.

Problem

• long-tailed distribution
• multi-label classification
• chest X-ray
• class imbalance
• rare diseases

Innovation

• generative data augmentation
• label co-occurrence modeling
• long-tailed classification
• text-guided diffusion
• class-aware attention