在联邦医疗物联网边缘环境中，用于两阶段肺结节分割和恶性肿瘤分类的自适应几何注意力网络
Adaptive geometric-attention network for two-stage lung nodule segmentation and malignancy classification in federated healthcare IoT edge environments.
摘要(中文译文)
在分布式医疗物联网环境中，计算机断层扫描（CT）图像中肺结节的精确分割与分类仍是早期肺癌检测的关键挑战。本文提出一种名为自适应几何注意力网络（AGA-Net）的新型两阶段框架，该框架将几何约束与多尺度注意力机制相结合，用于精确的结节分割，随后在联邦学习场景下进行不确定性感知的恶性分类。与依赖传统卷积架构的现有方法不同，我们的方法引入了几何约束注意力模块（GCAM），利用肺结节的球形特征，并采用多尺度不确定性量化网络（MUQ-Net）在隐私保护约束下实现稳健分类。所提出的框架在三个基准数据集LUNA16、LIDC-IDRI和NSCLC-Radiomics上展现出优越性能，分割的Dice系数达到0.927，恶性分类的AUC达到0.951，同时在包括NVIDIA Jetson AGX Orin和Jetson Orin Nano在内的物联网级边缘硬件上验证了计算效率。几何先验与注意力机制、不确定性量化及联邦学习能力的融合，提供了高准确性和临床可解释性，使其适用于可部署在医疗物联网边缘设备上的下一代计算机辅助诊断系统。
Abstract
Accurate segmentation and classification of lung nodules in computed tomography (CT) scans remains a critical challenge in early lung cancer detection within distributed healthcare Internet of Things (IoT) environments. This paper presents a novel two-stage framework called Adaptive Geometric-Attention Network (AGA-Net) that integrates geometric constraints with multi-scale attention mechanisms for precise nodule segmentation followed by uncertainty-aware malignancy classification in federated learning scenarios. Unlike existing approaches that rely on traditional convolutional architectures, our method introduces a Geometric-Constrained Attention Module (GCAM) that leverages the spherical nature of lung nodules and a Multi-Scale Uncertainty Quantification Network (MUQ-Net) for robust classification under privacy-preserving constraints. The proposed framework demonstrates superior performance across three benchmark datasets: LUNA16, LIDC-IDRI, and NSCLC-Radiomics, achieving a Dice coefficient of 0.927 for segmentation and AUC of 0.951 for malignancy classification while maintaining computational efficiency validated on IoT-class edge hardware including the NVIDIA Jetson AGX Orin and Jetson Orin Nano. The integration of geometric priors with attention mechanisms, uncertainty quantification, and federated learning capabilities provides both high accuracy and clinical interpretability, making it suitable for next-generation computer-aided diagnosis systems deployable on healthcare IoT edge devices.
如何引用
AMA
Muhammad Sufyan, Jun Qian, Jianqiang Li, Azhar Imran, Fahad Sabah, Raheem Sarwar. Adaptive geometric-attention network for two-stage lung nodule segmentation and malignancy classification in federated healthcare IoT edge environments.. PloS one. 2026; doi:10.1371/journal.pone.0341096.
APA
Muhammad Sufyan, Jun Qian, Jianqiang Li, Azhar Imran, Fahad Sabah, Raheem Sarwar (2026). Adaptive geometric-attention network for two-stage lung nodule segmentation and malignancy classification in federated healthcare IoT edge environments.. PloS one. https://doi.org/10.1371/journal.pone.0341096
GB/T 7714
Muhammad Sufyan, Jun Qian, Jianqiang Li, Azhar Imran, Fahad Sabah, Raheem Sarwar. Adaptive geometric-attention network for two-stage lung nodule segmentation and malignancy classification in federated healthcare IoT edge environments.[J]. PloS one, 2026 doi:10.1371/journal.pone.0341096.
在 PubMed增强版 阅读全文
翻译全文 · AI 文献问答 · PDF 原文下载 · 引用导出