Lung cancer follow-up is a complex, error prone, and time consuming task for
clinical radiologists. Several lung CT scan images taken at different time
points of a given patient need to be individually inspected, looking for
possible cancerogenous nodules. Radiologists mainly focus their attention in
nodule size, density, and growth to assess the existence of malignancy. In this
study, we present a novel method based on a 3D siamese neural network, for the
re-identification of nodules in a pair of CT scans of the same patient without
the need for image registration. The network was integrated into a two-stage
automatic pipeline to detect, match, and predict nodule growth given pairs of
CT scans. Results on an independent test set reported a nodule detection
sensitivity of 94.7%, an accuracy for temporal nodule matching of 88.8%, and a
sensitivity of 92.0% with a precision of 88.4% for nodule growth detection.

该研究提出了一种基于三重神经网络的新方法，用于在同一患者的两次 CT 扫描中识别癌变结节并检测其生长情况，结果表明识别敏感度为 94.7％，匹配准确率为 88.8％，生长检测的灵敏度为 92.0％，精度为 88.4％。

3D Siamese 神经网络实现无图像配准的肺结节再识别和增长检测

Re-Identification and Growth Detection of Pulmonary Nodules without  Image Registration Using 3D Siamese Neural Networks

In this work, we present a fully automated lung computed tomography (CT)
cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule
detection (identifying the locations of candidate nodules) and classification
(classifying candidate nodules into benign or malignant). Considering the 3D
nature of lung CT data and the compactness of dual path networks (DPN), two
deep 3D DPN are designed for nodule detection and classification respectively.
Specifically, a 3D Faster Regions with Convolutional Neural Net (R-CNN) is
designed for nodule detection with 3D dual path blocks and a U-net-like
encoder-decoder structure to effectively learn nodule features. For nodule
classification, gradient boosting machine (GBM) with 3D dual path network
features is proposed. The nodule classification subnetwork was validated on a
public dataset from LIDC-IDRI, on which it achieved better performance than
state-of-the-art approaches and surpassed the performance of experienced
doctors based on image modality. Within the DeepLung system, candidate nodules
are detected first by the nodule detection subnetwork, and nodule diagnosis is
conducted by the classification subnetwork. Extensive experimental results
demonstrate that DeepLung has performance comparable to experienced doctors
both for the nodule-level and patient-level diagnosis on the LIDC-IDRI
dataset.\footnote{this https URL}

本论文提出了基于自动化的深度学习算法的 Lung CT 肺癌诊断系统 DeepLung，该系统由结节检测和分类两个组件组成，其中结节检测使用 3D Faster R-CNN 和 3D 双路径块，结节分类使用 3D 双路径网络特征的梯度提升机（GBM）进行，DeepLung 系统在 LIDC-IDRI 公共数据集上的实验结果表明，该系统在结节级别和患者级别诊断性能与经验医生相当。

DeepLung: 深度 3D 双路径网络用于自动肺结节检测和分类

DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule  Detection and Classification

Automatic diagnosing lung cancer from Computed Tomography (CT) scans involves
two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the
whole-lung/pulmonary malignancy. Currently, there are many studies about the
first step, but few about the second step. Since the existence of nodule does
not definitely indicate cancer, and the morphology of nodule has a complicated
relationship with cancer, the diagnosis of lung cancer demands careful
investigations on every suspicious nodule and integration of information of all
nodules. We propose a 3D deep neural network to solve this problem. The model
consists of two modules. The first one is a 3D region proposal network for
nodule detection, which outputs all suspicious nodules for a subject. The
second one selects the top five nodules based on the detection confidence,
evaluates their cancer probabilities and combines them with a leaky noisy-or
gate to obtain the probability of lung cancer for the subject. The two modules
share the same backbone network, a modified U-net. The over-fitting caused by
the shortage of training data is alleviated by training the two modules
alternately. The proposed model won the first place in the Data Science Bowl
2017 competition. The code has been made publicly available.

使用 3D 深度神经网络和机器学习算法，检测和分析 CT 扫描中疑似肺结节病变的信息，用于肺癌自动诊断。

使用 3D 深度漏气噪声网络评估肺结节的恶性程度

Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky  Noisy-or Network

In this work, we present a fully automated lung CT cancer diagnosis system,
DeepLung. DeepLung contains two parts, nodule detection and classification.
Considering the 3D nature of lung CT data, two 3D networks are designed for the
nodule detection and classification respectively. Specifically, a 3D Faster
R-CNN is designed for nodule detection with a U-net-like encoder-decoder
structure to effectively learn nodule features. For nodule classification,
gradient boosting machine (GBM) with 3D dual path network (DPN) features is
proposed. The nodule classification subnetwork is validated on a public dataset
from LIDC-IDRI, on which it achieves better performance than state-of-the-art
approaches, and surpasses the average performance of four experienced doctors.
For the DeepLung system, candidate nodules are detected first by the nodule
detection subnetwork, and nodule diagnosis is conducted by the classification
subnetwork. Extensive experimental results demonstrate the DeepLung is
comparable to the experienced doctors both for the nodule-level and
patient-level diagnosis on the LIDC-IDRI dataset.

本文介绍一种基于深度学习的肺部 CT 自动癌症诊断系统 DeepLung，包括两个部分：结节检测和结节分类，两个 3D 网络分别应用于它们，该系统在 LIDC-IDRI 数据集上获得了与经验医生相似的结节和患者级别的诊断性能。