论文标题
钢琴：金字塔输入增强卷积神经网络，用于3D肺CT扫描中的GGO检测
PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scans
论文作者
论文摘要
本文提出了一个新的卷积神经网络，其中具有多尺度处理，用于检测3D计算机断层扫描（CT）图像中的地面玻璃不透明度（GGO）结节，该图像称为钢板。钢琴由特征萃取模块和一个预测模块组成。以前的模块是通过将金字塔多尺度源连接引入收缩结构来构建的。后一个模块包含一个边界盒回归仪和一个分类器，这些回归仪可同时识别GGO结节并在多个尺度上估算边界框。为了训练拟议的钢琴，制定了两阶段的转移学习策略。在第一阶段，将特征萃取模块嵌入到一个分类器网络中，该模块在大量的GGO和非GGGO补丁的数据集上进行了训练，这些数据集是通过从少量注释的CT扫描中执行数据增强而生成的。在第二阶段，验证的特征 - 萃取模块被加载到钢琴中，然后使用带注释的CT扫描对钢板进行微调。我们在LIDC-IDRI数据集上评估了所提出的钢板。实验结果表明，我们的方法的表现优于最先进的对应物，包括subsolid CAD和AIDENS Systems以及S4ND和GA-SSD方法。钢板的灵敏度为91.75％，每次扫描仅一个假阳性
This paper proposes a new convolutional neural network with multiscale processing for detecting ground-glass opacity (GGO) nodules in 3D computed tomography (CT) images, which is referred to as PiaNet for short. PiaNet consists of a feature-extraction module and a prediction module. The former module is constructed by introducing pyramid multiscale source connections into a contracting-expanding structure. The latter module includes a bounding-box regressor and a classifier that are employed to simultaneously recognize GGO nodules and estimate bounding boxes at multiple scales. To train the proposed PiaNet, a two-stage transfer learning strategy is developed. In the first stage, the feature-extraction module is embedded into a classifier network that is trained on a large data set of GGO and non-GGO patches, which are generated by performing data augmentation from a small number of annotated CT scans. In the second stage, the pretrained feature-extraction module is loaded into PiaNet, and then PiaNet is fine-tuned using the annotated CT scans. We evaluate the proposed PiaNet on the LIDC-IDRI data set. The experimental results demonstrate that our method outperforms state-of-the-art counterparts, including the Subsolid CAD and Aidence systems and S4ND and GA-SSD methods. PiaNet achieves a sensitivity of 91.75% with only one false positive per scan
