用CBAM增强CNN模型，提升胸部X光多病种诊断准确率
Improving Imbalanced Multi-Label Chest X-Ray Diagnosis via CBAM-Enhanced CNN Backbones
- 将CBAM模块嵌入传统CNN，动态优化特征提取
- 在ChestXray14数据集上平均AUC达0.8695，优于多个先进方法
- 适合处理病灶类别不平衡的医学影像多标签分类任务
胸部放射成像广泛用于胸腔疾病诊断，但传统解读耗时且依赖专家经验。深度学习虽通过自动特征提取提升了诊断效率，但仍面临类别不平衡和多种病灶共存定位难题。本文受卷积块注意力模块（CBAM）在特征精炼中的优势及CNN块特征提取能力启发，提出将CBAM融入传统CNN结构，以增强多标签分类性能。实验表明，该方法在ChestXray14数据集上取得0.8695的平均AUC，超越多个现有先进基线模型。代码已公开于https://github.com/NNNguyenDuyyy/FETC_CBAM_Enhanced_CNN.git。
原文摘要 · Abstract (English)
Chest radiography is a widely used imaging modality for thoracic disease diagnosis, yet its conventional interpretation remains time-consuming and heavily dependent on expert knowledge. While deep learning has improved diagnostic efficiency through automated feature extraction, challenges such as class imbalance and the localization of multiple co-existing pathologies remain unsolved. In this paper, inspired by the strength of Convolutional Block Attention Module (CBAM) in feature refinement and the capability of CNN blocks in feature extraction, we propose a strategy to integrate CBAM into traditional CNN blocks to enhance performance in multi-label classification tasks. Our method achieves a mean AUC of 0.8695 on ChestXray14 dataset, outperforming several state-of-the-art baselines.Our source code is available at: https://github.com/NNNguyenDuyyy/FETC_CBAM_Enhanced_CNN.git
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