新结构提升小肺结节检测精度，助力早期肺癌筛查
Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation
- 用通道压缩U型结构融合多层级特征，增强细节捕捉能力
- 在LIDC数据集上达94.3%敏感度，Dice系数超0.85
- 适合临床辅助诊断，尤其资源有限地区使用
本文提出一种基于深度学习的肺结节自动检测与分割方法，旨在提升早期肺癌诊断的准确性。所提方法采用独特的“通道压缩U型结构”，优化网络中多个语义层级的特征提取与信息融合。该架构包含浅层信息处理、通道残差结构和通道压缩集成三个关键模块，显著提升了对微小、难以察觉或磨玻璃样肺结节的检测与分割能力，这些结节对早期诊断至关重要。在肺影像数据库联盟（LIDC）数据集上，通过五折交叉验证进行大量实验，结果表明该方法在敏感度、Dice相似系数、精确率和平均交并比（mIoU）方面均表现优异，展现出良好的稳定性和鲁棒性。该方法具有显著潜力，可有效提升计算机辅助诊断系统性能，为放射科医生提供可靠支持，尤其在资源有限的临床环境中，有助于实现肺癌早期发现。
原文摘要 · Abstract (English)
This paper introduces a novel deep-learning method for the automatic detection and segmentation of lung nodules, aimed at advancing the accuracy of early-stage lung cancer diagnosis. The proposed approach leverages a unique "Channel Squeeze U-Structure" that optimizes feature extraction and information integration across multiple semantic levels of the network. This architecture includes three key modules: shallow information processing, channel residual structure, and channel squeeze integration. These modules enhance the model's ability to detect and segment small, imperceptible, or ground-glass nodules, which are critical for early diagnosis. The method demonstrates superior performance in terms of sensitivity, Dice similarity coefficient, precision, and mean Intersection over Union (IoU). Extensive experiments were conducted on the Lung Image Database Consortium (LIDC) dataset using five-fold cross-validation, showing excellent stability and robustness. The results indicate that this approach holds significant potential for improving computer-aided diagnosis systems, providing reliable support for radiologists in clinical practice and aiding in the early detection of lung cancer, especially in resource-limited settings
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