随着胸部计算机断层扫描（CT）筛查的普及，发现需要进一步诊断的亚厘米（≤1 cm）肺结节（SCPN）数量也增加了。此领域代表了一个重要的机会，可以优化SCPN管理算法，避免“一刀切”的方法。一个关键问题是如何学习区分性多视图特征和每个SCPN的独特上下文。在这里，我们提出了一个多视图耦合自注意模块（MVCS），通过建模空间和维度的关联顺序来捕捉CT图像的全局空间背景。与现有的自注意方法相比，MVCS使用的内存消耗更小、计算复杂度更低，发现了以前的方法未发现的维度相关性，且易于与其他框架集成。总共使用了来自LIDC-IDRI的公共数据集LUNA16，包括来自一个重要转诊中心的1069名患者的1319个SCPN以及来自其他三个重要中心的137名患者的160个SCPN，进行了预训练、训练和验证模型。实验结果表明，性能在准确性和稳定性方面优于最先进的模型，并且与人类专家在分类癌前病变和浸润性腺癌方面具有可比性。我们还结合了患者的临床特征和放射学特征，提供了一个融合MVCS网络（MVCSN）。这个工具最终可以帮助加速恶性SCPN的切除，避免对良性SCPN的过度诊断，从而改善管理结果。在亚厘米肺腺癌的诊断中，融合MVCSN可以帮助医生提高工作效率，并在一定程度上指导他们的治疗决策。• 计算机断层扫描（CT）的进展不仅增加了发现的结节数量，还使得发现的结节更小，例如亚厘米肺结节（SCPN）。• 我们提出了一个多视图耦合自注意模块（MVCS），该模块可以顺序地模拟空间和维度之间的关联，以学习全局空间背景，这比其他注意机制更好。• 在处理3D医学影像数据时，与现有的自注意方法相比，MVCS的内存消耗更小、计算复杂度更低。此外，它在评估SCPN的恶性程度方面能够达到有希望的准确性，并且训练成本较低于其他模型。© 2023。这是一项美国政府的工作，不受美国版权保护，可能适用外国版权保护。

With the popularization of chest computed tomography (CT) screening, there are more sub-centimeter (≤ 1 cm) pulmonary nodules (SCPNs) requiring further diagnostic workup. This area represents an important opportunity to optimize the SCPN management algorithm avoiding "one-size fits all" approach. One critical problem is how to learn the discriminative multi-view characteristics and the unique context of each SCPN.Here, we propose a multi-view coupled self-attention module (MVCS) to capture the global spatial context of the CT image through modeling the association order of space and dimension. Compared with existing self-attention methods, MVCS uses less memory consumption and computational complexity, unearths dimension correlations that previous methods have not found, and is easy to integrate with other frameworks.In total, a public dataset LUNA16 from LIDC-IDRI, 1319 SCPNs from 1069 patients presenting to a major referral center, and 160 SCPNs from 137 patients from three other major centers were analyzed to pre-train, train, and validate the model. Experimental results showed that performance outperforms the state-of-the-art models in terms of accuracy and stability and is comparable to that of human experts in classifying precancerous lesions and invasive adenocarcinoma. We also provide a fusion MVCS network (MVCSN) by combining the CT image with the clinical characteristics and radiographic features of patients.This tool may ultimately aid in expediting resection of the malignant SCPNs and avoid over-diagnosis of the benign ones, resulting in improved management outcomes.In the diagnosis of sub-centimeter lung adenocarcinoma, fusion MVCSN can help doctors improve work efficiency and guide their treatment decisions to a certain extent.• Advances in computed tomography (CT) not only increase the number of nodules detected, but also the nodules that are identified are smaller, such as sub-centimeter pulmonary nodules (SCPNs). • We propose a multi-view coupled self-attention module (MVCS), which could model spatial and dimensional correlations sequentially for learning global spatial contexts, which is better than other attention mechanisms. • MVCS uses fewer huge memory consumption and computational complexity than the existing self-attention methods when dealing with 3D medical image data. Additionally, it reaches promising accuracy for SCPNs' malignancy evaluation and has lower training cost than other models.© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.