Pulmonary nodules risk classification in adenocarcinoma is essential for early detection of lung cancer and clinical treatment decision. Improving the level of early diagnosis and the identification of small lung adenocarcinoma has been always an important topic for imaging studies. In this study, the authors propose a deep convolutional neural network (CNN) with scale-transfer module (STM) and incorporate multi-feature fusion operation, named STM-Net. This network can amplify small targets and adapt to different resolution images. The evaluation data were obtained from the computed tomography (CT) database provided by Zhongshan Hospital Fudan University (ZSDB). All data have a pathological label and their lung adenocarcinomas risk are classified into four categories: atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma. The authors’ deep learning network STM-Net was trained and tested for the risk stage prediction. The accuracy and the average area under the receiver operating characteristic curve achieved by their method are 95.455% and 0.987 for the ZSDB dataset. The experimental results show that STM-Net largely boosts classification accuracy on the pulmonary nodules classification compared with state-of-the-art approaches. The proposed method will be an effective auxiliary to help physicians diagnosis pulmonary nodules risk classification in adenocarcinoma in early-stage.

中文翻译：

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使用深层CNN和尺度转移模块从CT图像对腺癌进行肺结节风险分类

腺癌的肺结节风险分类对于肺癌的早期发现和临床治疗决策至关重要。提高早期诊断水平和鉴定小肺腺癌一直是影像学研究的重要课题。在这项研究中，作者提出了带有尺度传递模块（STM）的深度卷积神经网络（CNN），并结合了名为STM-Net的多特征融合操作。该网络可以放大小目标并适应不同分辨率的图像。评估数据来自复旦大学附属中山医院计算机断层扫描（CT）数据库。所有数据都有病理标记，其肺腺癌风险分为四类：非典型腺瘤增生，原位腺癌，微浸润性腺癌和浸润性腺癌。作者的深度学习网络STM-Net已针对风险阶段预测进行了培训和测试。对于ZSDB数据集，通过他们的方法获得的接收机工作特性曲线下的准确度和平均面积分别为95.455％和0.987。实验结果表明，与最新技术相比，STM-Net大大提高了肺结节分类的分类准确性。该方法将为早期诊断腺癌的肺结节风险分类提供有效的辅助手段。对于ZSDB数据集，通过他们的方法获得的接收器工作特性曲线下的准确度和平均面积分别为95.455％和0.987。实验结果表明，与最新技术相比，STM-Net大大提高了肺结节分类的分类准确性。该方法将为早期诊断腺癌的肺结节风险分类提供有效的辅助手段。对于ZSDB数据集，通过他们的方法获得的接收机工作特性曲线下的准确度和平均面积分别为95.455％和0.987。实验结果表明，与最新技术相比，STM-Net大大提高了肺结节分类的分类准确性。该方法将为早期诊断腺癌的肺结节风险分类提供有效的辅助手段。