Lung cancer is the leading cause of cancer death and early diagnosis is associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to distinguish from vascular and bone structures using CXR. Computer vision has previously been proposed to assist human radiologists in this task, however, leading studies use down-sampled images and computationally expensive methods with unproven generalization. Instead, this study localizes lung nodules using efficient encoder-decoder neural networks that process full resolution images to avoid any signal loss resulting from down-sampling. Encoder-decoder networks are trained and tested using the JSRT lung nodule dataset. The networks are used to localize lung nodules from an independent external CXR dataset. Sensitivity and false positive rates are measured using an automated framework to eliminate any observer subjectivity. These experiments allow for the determination of the optimal network depth, image resolution and pre-processing pipeline for generalized lung nodule localization. We find that nodule localization is influenced by subtlety, with more subtle nodules being detected in earlier training epochs. Therefore, we propose a novel self-ensemble model from three consecutive epochs centered on the validation optimum. This ensemble achieved a sensitivity of 85% in 10-fold internal testing with false positives of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6 following morphological false positive reduction. This result is comparable to more computationally complex systems based on linear and spatial filtering, but with a sub-second inference time that is faster than other methods. The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7.6.

翻译：肺癌是癌症死亡的主要原因，早期诊断与良好预后相关。胸部X光（CXR）为肺癌诊断提供了一种廉价的成像方式。通过CXR，可疑结节难以与血管和骨骼结构区分。此前，计算机视觉已被提出用于辅助人类放射科医生完成此任务，然而，主流研究使用降采样图像和计算成本高昂且泛化能力未经证实的方法。相反，本研究采用高效的编码器-解码器神经网络来定位肺结节，这些网络处理全分辨率图像，以避免因降采样导致的任何信号丢失。使用JSRT肺结节数据集对编码器-解码器网络进行训练和测试。这些网络用于从独立的外部CXR数据集中定位肺结节。使用自动化框架测量敏感性和假阳性率，以消除任何观察者主观性。这些实验能够确定用于泛化肺结节定位的最佳网络深度、图像分辨率和预处理流程。我们发现，结节定位受微妙性影响，更微妙的结节在较早的训练周期中被检测到。因此，我们提出了一种新颖的自集成模型，该模型由以验证最佳值为中心的三个连续周期组成。在10折内部测试中，该集成实现了85%的敏感性，每张图像假阳性为8个。在形态学假阳性减少后，假阳性率为6时达到81%的敏感性。这一结果与基于线性和空间滤波的计算复杂度更高的系统相当，但推理时间低于0.1秒，快于其他方法。所提算法在外部数据集上取得了优异的泛化结果，假阳性率为7.6时敏感性为77%。