Recent advancements in deep learning have achieved great success in building a reliable computer-aided diagnosis (CAD) system. In this work, a novel deep-learning architecture, named receptive field regularized V-net (RFR V-Net), is proposed for detecting lung cancer nodules with reduced false positives (FP). The method uses a receptive regularization on the encoder block's convolution and deconvolution layer of the decoder block in the V-Net model. Further, nodule classification is performed using a new combination of SqueezeNet and ResNet, named nodule classification network (NCNet). Postprocessing image enhancement is performed on the 2D slice by increasing the image's intensity by adding pseudo-color or fluorescence contrast. The proposed RFR V-Net resulted in dice similarity coefficient of 95.01% and intersection over union of 0.83, respectively. The proposed NCNet achieved the sensitivity of 98.38% and FPs/Scan of 2.3 for 3D representations. The proposed NCNet resulted in considerable improvements over existing CAD systems.

中文翻译：

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一种用于肺结节检测的新型感受野正则化 V-net 和结节分类网络

深度学习的最新进展在构建可靠的计算机辅助诊断 (CAD) 系统方面取得了巨大成功。在这项工作中，提出了一种新的深度学习架构，称为感受野正则化 V-net (RFR V-Net)，用于检测肺癌结节并减少误报 (FP)。该方法对 V-Net 模型中解码器块的编码器块的卷积和反卷积层使用接受正则化。此外，结节分类是使用 SqueezeNet 和 ResNet 的新组合进行的，称为结节分类网络 (NCNet)。通过添加伪彩色或荧光对比度来增加图像的强度，对 2D 切片执行后处理图像增强。提出的 RFR V-Net 的骰子相似系数为 95.01%，交集比联合为 0.83，分别。所提出的 NCNet 对于 3D 表示实现了 98.38% 的灵敏度和 2.3 的 FPs/Scan。提议的 NCNet 对现有 CAD 系统产生了相当大的改进。