Computed tomography (CT) is an important and valuable tool for detecting and diagnosing lung cancer at an early stage. Commonly, CT screenings with lower dose and resolution are used for preliminary screening. In particular, many hospitals in smaller towns only provide CT screenings at low resolution. However,when patients are diagnosed with suspected cancer, they are transferred or recommended to larger hospitals for more sophisticated examinations with high-resolution CT scans. Therefore, multi-resolution CT images deserve attention and are critical in clinical practice. Currently, the available open source datasets only contain high-resolution CT screening images. To address this problem, a multi-resolution CT screening image dataset called the DeepLNDataset is constructed. A three-level labeling criterion and a semi-automatic annotation system are presented to guarantee the correctness and efficiency of lung nodule annotation. Moreover, a novel framework called DeepLN is proposed to detect lung nodules in both low-resolution and high-resolution CT screening images. The multi-level features are extracted by a neural-network based detector to locate the lung nodules. Hard negative mining and a modified focal loss function are employed to solve the common category imbalance problem. A novel non-maximum suppression based ensemble strategy is proposed to synthesize the results from multiple neural network models trained on CT image datasets of different resolutions. To the best of our knowledge, this is the first work that considers the influence of multiple resolutions on lung nodule detection. The experimental results demonstrate that the proposed method can address this issue well.

计算机断层扫描（CT）是早期检测和诊断肺癌的重要且有价值的工具。通常，较低剂量和分辨率的CT筛查用于初步筛查。特别是，小城镇中的许多医院仅提供低分辨率的CT筛查。但是，当患者被诊断出怀疑患有癌症时，他们会被转移或推荐到较大的医院进行高分辨率CT扫描以进行更复杂的检查。因此，多分辨率CT图像值得关注并且在临床实践中至关重要。当前，可用的开源数据集仅包含高分辨率的CT筛查图像。为了解决这个问题，构建了一个称为DeepLNDataset的多分辨率CT筛查图像数据集。提出了三级标记标准和半自动标注系统，以保证肺结节标注的正确性和有效性。此外，提出了一种称为DeepLN的新颖框架，以在低分辨率和高分辨率CT筛查图像中检测肺结节。通过基于神经网络的检测器提取多级特征以定位肺结节。硬负挖掘和修正的焦点损失函数被用来解决常见的类别不平衡问题。提出了一种新颖的基于非最大抑制的集成策略，以合成来自在不同分辨率的CT图像数据集上训练的多个神经网络模型的结果。据我们所知，这是考虑多种分辨率对肺结节检测影响的第一项工作。