Song Xinhang, Xie Haoran, Gao Tianding, Cheng Nuo, Gou Jianping
College of Computer and Information Science & College of Software, Southwest University, Beibei District, Chongqing 400715, China.
Sensors (Basel). 2025 Jul 8;25(14):4245. doi: 10.3390/s25144245.
The accurate identification of pulmonary nodules is critical for the early diagnosis of lung diseases; however, this task remains challenging due to inadequate feature representation and limited localization sensitivity. Current methodologies often utilize channel attention mechanisms and intersection over union (IoU)-based loss functions. Yet, they frequently overlook spatial context and struggle to capture subtle variations in aspect ratios, which hinders their ability to detect small objects. In this study, we introduce an improved YOLOV11 framework that addresses these limitations through two primary components: a spatial squeeze-and-excitation (SSE) module that concurrently models channel-wise and spatial attention to enhance the discriminative features pertinent to nodules and explicit aspect ratio penalty IoU (EAPIoU) loss that imposes a direct penalty on the squared differences in aspect ratios to refine the bounding box regression process. Comprehensive experiments conducted on the LUNA16, LungCT, and Node21 datasets reveal that our approach achieves superior precision, recall, and mean average precision (mAP) across various IoU thresholds, surpassing previous state-of-the-art methods while maintaining computational efficiency. Specifically, the proposed SSE module achieves a precision of 0.781 on LUNA16, while the EAPIoU loss boosts mAP@50 to 92.4% on LungCT, outperforming mainstream attention mechanisms and IoU-based loss functions. These findings underscore the effectiveness of integrating spatially aware attention mechanisms with aspect ratio-sensitive loss functions for robust nodule detection.
肺结节的准确识别对于肺部疾病的早期诊断至关重要；然而，由于特征表示不足和定位灵敏度有限，这项任务仍然具有挑战性。当前的方法通常利用通道注意力机制和基于交并比（IoU）的损失函数。然而，它们常常忽略空间上下文，并且难以捕捉宽高比的细微变化，这阻碍了它们检测小物体的能力。在本研究中，我们引入了一种改进的YOLOV11框架，该框架通过两个主要组件解决了这些局限性：一个空间挤压与激励（SSE）模块，它同时对通道注意力和空间注意力进行建模，以增强与结节相关的判别特征；以及显式宽高比惩罚IoU（EAPIoU）损失，它对宽高比的平方差施加直接惩罚，以优化边界框回归过程。在LUNA16、LungCT和Node21数据集上进行的综合实验表明，我们的方法在各种IoU阈值下都实现了卓越的精度、召回率和平均精度均值（mAP），超越了先前的先进方法，同时保持了计算效率。具体而言，所提出的SSE模块在LUNA16上实现了0.781的精度，而EAPIoU损失在LungCT上将mAP@50提高到了92.4%，优于主流的注意力机制和基于IoU的损失函数。这些发现强调了将空间感知注意力机制与宽高比敏感损失函数相结合用于稳健结节检测的有效性。
