基于3D CNN的肺结节假阳性筛查模型

通过肺部CT影像进行肺结节检测是肺癌早期筛查的重要手段,而候选结节的假阳性筛查是结节检测的关键部分.传统的结节检测方法严重依赖先验知识,流程繁琐,性能并不理想.在深度学习中,卷积神经网络可以在通用的学习过程中提取图像的特征.该文以密集神经网络为基础设计了一个三维结节假阳性筛查模型—三维卷积神经网络模型(TDN-CNN)...     

基于深度混合卷积模型的肺结节检测方法

基于高维肺部计算机断层扫描（CT）图像的肺结节检测是一项极具挑战性的任务。在诸多肺结节检测算法中,深度卷积神经网络（CNN）最引人注目,其中二维（2D） CNN具有预训练模型多、检测效率高等优点,应用非常广泛,但肺结节本质是三维（3D）病灶,2D CNN会不可避免地造成信息损失,从而影响检测精度。3D CNN能充分利用CT图像空间信息,有效提升检测精度,但是3D CNN存在参数多、计算消耗大、过拟合风险高等不足。为了兼顾两者的优势,提出基于深度混合CNN的肺结节检测模型,通过在神经网络模型的浅层部署3D CNN,在模型的深层部署2D CNN,并增加反卷积模块,融合了多层级的图像特征,达到了在不损失检测精度的情况下减少模型参数、增强模型泛化能力,提高检测效率的目的。在LUNA16数据集上的实验结果表明,所提出的模型在平均每次扫描8个假阳性的情况下的敏感度为0.924,优于现有的先进模型。     

基于深度混合卷积模型的肺结节检测方法

基于高维肺部计算机断层扫描（CT）图像的肺结节检测是一项极具挑战性的任务。在诸多肺结节检测算法中，深度卷积神经网络（CNN）最引人注目，其中二维（2D） CNN具有预训练模型多、检测效率高等优点，应用非常广泛，但肺结节本质是三维（3D）病灶，2D CNN会不可避免地造成信息损失，从而影响检测精度。3D CNN能充分利用CT图像空间信息，有效提升检测精度，但是3D CNN存在参数多、计算消耗大、过拟合风险高等不足。为了兼顾两者的优势，提出基于深度混合CNN的肺结节检测模型，通过在神经网络模型的浅层部署3D CNN，在模型的深层部署2D CNN，并增加反卷积模块，融合了多层级的图像特征，达到了在不损失检测精度的情况下减少模型参数、增强模型泛化能力，提高检测效率的目的。在LUNA16数据集上的实验结果表明，所提出的模型在平均每次扫描8个假阳性的情况下的敏感度为0.924，优于现有的先进模型。     

基于卷积神经网络的胸片肺野自动分割

针对传统胸片肺野分割方法需要人工干预、提取特征以及对先验知识的依赖性问题,提出了一种基于卷积神经网络(CNN)的胸片肺野自动分割方法,将X光胸片的分割问题转换为图像块的分类问题.将原图像分割成左、右肺,切块处理后分别作为训练样本,利用深度学习自动发现图像块中的潜在特征,对图像块进行分类,并将结果映射成二值图,得到初步分割结果,再对其进行后处理,合并之后作为最终的分割结果.实验表明:此方法在公开的JSRT数据集上进行测试,Jaccard指标可达94.6%,平均边界距离(MBD)指标达到1.10 mm,较现存分割算法更加出色.     

Dual-branch residual network for lung nodule segmentation

An accurate segmentation of lung nodules in computed tomography (CT) images is critical to lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the similarity of visual characteristics between nodules and their surroundings, a robust segmentation of nodules becomes a challenging problem. In this study, we propose the Dual-branch Residual Network (DB-ResNet) which is a data-driven model. Our approach integrates two new schemes to improve the generalization capability of the model: (1) the proposed model can simultaneously capture multi-view and multi-scale features of different nodules in CT images; (2) we combine the features of the intensity and the convolutional neural networks (CNN). We propose a pooling method, called the central intensity-pooling layer (CIP), to extract the intensity features of the center voxel of the block, and then use the CNN to obtain the convolutional features of the center voxel of the block. In addition, we designed a weighted sampling strategy based on the boundary of nodules for the selection of those voxels using the weighting score, to increase the accuracy of the model. The proposed method has been extensively evaluated on the LIDC-IDRI dataset containing 986 nodules. Experimental results show that the DB-ResNet achieves superior segmentation performance with the dice similarity coefficient (DSC) of 82.74% on the dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison showed that our DSC was 0.49% higher than that of human experts. This proves that our proposed method is as good as the experienced radiologist.     

A central-point-enhanced convolutional neural network for high-resolution remote-sensing image classification

As one of the most important algorithms in the field of deep learning technology, the convolutional neural network (CNN) has been successfully applied in many fields. CNNs can recognize objects in an image by considering morphology and structure rather than simply individual pixels. One advantage of CNNs is that they exhibit translational invariance; when an image contains a certain degree of distortion or shift, a CNN can still recognize the object in the image. However, this advantage becomes a disadvantage when CNNs are applied to pixel-based classification of remote-sensing images, because their translational invariance characteristics causes distortions in land-cover boundaries and outlines in the classification result image. This problem severely limits the application of CNNs in remote-sensing classification. To solve this problem, we propose a central-point-enhanced convolutional neural network (CE-CNN) to classify high-resolution remote-sensing images. By introducing the central-point-enhanced layer when classifying a sample, the CE-CNN increases the weight of the central point in feather maps while preserving the original textures and characteristics. In our experiment, we selected four representative positions on a high-resolution remote-sensing image to test the classification ability of the proposed method and compared the CE-CNN with the traditional multi-layer perceptron (MLP) and a traditional CNN. The results show that the proposed method can not only achieves a higher classification accuracy but also less distortion and fewer incorrect results at the boundaries of land covers. We further compared the CE-CNN with six state-of-the-art methods: k-NN, maximum likelihood, classification and regression tree (CART), MLP, support vector machine, and CNN. The results show that the CE-CNN’s classification accuracy is better than the other methods.     

深度学习在肺结节辅助诊断中的应用

肺癌位居癌症死亡率首位,对其进行早期诊断和治疗可降低肺癌患者的死亡率。深度学习能够自动提取结节特征,并完成肺结节的良恶性及恶性等级分类,因此深度学习方法成为肺癌早期诊断的重要手段。对常用数据集进行介绍,系统阐述了栈式去噪自编码器（SDAE）、深度置信网络（DBN）、生成对抗网络（GAN）、卷积神经网络（CNN）、循环神经网络（RNN）和迁移学习技术在肺结节良恶性分类中的应用,阐述了深度卷积生成对抗网络（DCGAN）、多尺度卷积神经网络（MCNN）、U型网络（U-Net）和集成学习技术在肺结节恶性等级分类中的应用,针对肺结节分类的深度学习方法进行了综合分析,并对未来研究方向进行展望。     

基于深度卷积神经网络的肺结节检测算法

在传统的肺结节检测算法中，存在检测敏感度低，假阳性数量大的问题。针对这一问题，提出了基于深度卷积神经网络（CNN）的肺结节检测算法。首先，有目的性地简化传统的全卷积分割网络；然后，创新地加入对部分CNN层的深监督并使用改进的加权损失函数，获得高质量的候选肺结节，保证高敏感度；其次，设计了基于多尺度上下文信息的三维深度CNN来增强对图像的特征提取；最后，将训练得到的融合分类模型用于候选结节分类，以达到降低假阳率的目的。所提算法使用了LUNA16数据集，并通过对比实验验证算法的性能。在检测阶段，当每个CT检测出的候选结节数为50.2时，获得的敏感度为94.3%，与传统的全卷积分割网络相比提升了4.2个百分点；在分类阶段，竞争性能指标达到0.874。实验结果表明，所提算法能够有效提高检测敏感度和降低假阳率。     

Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Tuberculosis (TB) is a severe infection that mostly affects the lungs and kills millions of people’s lives every year. Tuberculosis can be diagnosed using chest X-rays (CXR) and data-driven deep learning (DL) approaches. Because of its better automated feature extraction capability, convolutional neural networks (CNNs) trained on natural images are particularly effective in image categorization. A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets. Ten different deep CNNs (Resnet50, Resnet101, Resnet152, InceptionV3, VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, MobileNet) are trained and tested for identifying TB and normal cases. This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmentation of interest, and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection. Furthermore, this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs. It was able to accurately detect TB from CXR images using pre-processing, data augmentation, and deep CNN models. Without histogram matching the best accuracy, sensitivity, specificity, precision and F1-score in the detection of TB using CXR images among ten models are 99.25%, 99.48%, 99.52%, 99.48% and 99.22% respectively. With histogram matching the best accuracy, sensitivity, specificity, precision and F1-score are 99.58%, 99.82%, 99.67%, 99.65% and 99.56% respectively. The proposed methodology, which has cutting-edge performance, will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.     

Convolutional neural network based heterogeneous transfer learning for remote-sensing scene classification

ABSTRACT

Deep convolutional neural network (CNN) transfer has recently shown strong performance in scene classification of high-resolution remote-sensing images. However, the majority of transfer learning solutions are categorized as homogeneous transfer learning, which ignores differences between target and source domains. In this paper, we propose a heterogeneous model to transfer CNNs to remote-sensing scene classification to correct input feature differences between target and source datasets. First, we extract filters from source images using the principal component analysis (PCA) method. Next, we convolute the target images with the extracted PCA filters to obtain an adopted target dataset. Then, a pretrained CNN is transferred to the adopted target dataset as a feature extractor. Finally, a classifier is used to accomplish remote-sensing scene classification. We conducted extensive experiments on the UC Merced dataset, the Brazilian coffee scene dataset and the Aerial Images Dataset to verify the effectiveness of the proposed heterogeneous model. The experimental results show that the proposed heterogeneous model outperforms the homogeneous model that uses pretrained CNNs as feature extractors by a wide margin and gains similar accuracies by fine-tuning a homogeneous transfer learning model with few training iterations.     

基于混合损失联合调优与多尺度分类相结合的肺结节检测算法

针对CT图像的肺结节自动检测任务中检测灵敏度低及存在大量假阳性的问题,提出了一个基于混合损失的三维全卷积网络与基于注意力的多尺度三维残差网络相结合的肺结节检测方法。首先,基于相似度损失预训练三维全卷积网络,利用该网络筛选难例样本,并基于混合损失将难例与正样本进行联合调优得到候选结节检测网络,用于快速筛选疑似结节;然后,利用基于注意力的多尺度三维残差卷积网络对疑似结节进行分类,从候选结节中精确地分辨出真正结节。在LUN16数据集上,候选结节检测阶段的灵敏度在每个病例的假阳数目为59.1时达到97.18%,检测系统的平均灵敏度为0.880,表明本算法可以提高肺结节检测的灵敏度并有效控制假阳性,在LUNA16数据集上获得了更优的性能。     

融合多维度卷积神经网络的肺结节分类方法

针对CT图像肺结节分类任务中分类精度低,假阳性高的问题,提出了一种加权融合多维度卷积神经网络的肺结节分类模型,该模型包含两个子模型：基于二维图像的多尺度密集卷积网络模型,以捕获更宽泛的结节变化特征并促进特征重用;基于三维图像的三维卷积神经网络模型,以充分利用结节空间上下文信息。使用二维和三维CT图像训练子模型,根据子模型分类误差计算其权重,对子模型分类结果进行加权融合,得到最终分类结果。该模型在公共数据集LIDC-IDRI上分类准确率达到94.25%,AUC值达到98%。实验结果表明,加权融合多维度模型可以有效地提升肺结节分类性能。     

Real-time COVID-19 detection over chest x-ray images in edge computing

Severe Coronavirus Disease 2019 (COVID-19) has been a global pandemic which provokes massive devastation to the society, economy, and culture since January 2020. The pandemic demonstrates the inefficiency of superannuated manual detection approaches and inspires novel approaches that detect COVID-19 by classifying chest x-ray (CXR) images with deep learning technology. Although a wide range of researches about bran-new COVID-19 detection methods that classify CXR images with centralized convolutional neural network (CNN) models have been proposed, the latency, privacy, and cost of information transmission between the data resources and the centralized data center will make the detection inefficient. Hence, in this article, a COVID-19 detection scheme via CXR images classification with a lightweight CNN model called MobileNet in edge computing is proposed to alleviate the computing pressure of centralized data center and ameliorate detection efficiency. Specifically, the general framework is introduced first to manifest the overall arrangement of the computing and information services ecosystem. Then, an unsupervised model DCGAN is employed to make up for the small scale of data set. Moreover, the implementation of the MobileNet for CXR images classification is presented at great length. The specific distribution strategy of MobileNet models is followed. The extensive evaluations of the experiments demonstrate the efficiency and accuracy of the proposed scheme for detecting COVID-19 over CXR images in edge computing.     

基于多尺度多模式图像的肺结节分类对比研究

基于深度卷积神经网络模型,讨论了不同尺度及不同模式肺结节图像对模型分类表现的影响,并提出了一种2D多视图融合的肺图像处理方法,该方法比传统的2D方式能获取更多的肺结节信息,同时又能比3D的方式引入更少的干扰组织。为了验证模型,对LIDC-IDRI和LUNA16数据集进行了预处理,得到了16、25、36三种尺度下2D、3D、2D全视图融合以及2D多视图融合四种不同模式的肺结节图像,然后构建了2D CNN、3D CNN、2D全视图融合卷积神经网络、2D多视图融合卷积神经网络四种模型。利用上述样本对模型进行训练和验证,最终结果表明,2D多视图融合模式下的肺结节图像相对于其他模式图像具有更佳的肺结节分类表现;对比多种尺度图像,小尺度下的分类表现相对更佳。     

基于神经网络架构搜索的肺结节分类算法

肺结节分类是早期肺癌诊断的重要任务。基于深度学习的肺结节分类方法虽然能够取得良好的分类精度,但存在模型复杂和可解释性差的问题。为此,提出了一种基于神经网络架构搜索的肺结节分类算法。首先,将注意力残差卷积cell作为搜索空间的基本单元,并使用偏序剪枝方法作为搜索策略来构建神经网络架构以搜索3D分类网络,从而达到网络性能和搜索速度的平衡。其次,在网络中构建了多尺度通道和空间注意力模块来提高特征描述和类别推理的可解释性。最后,采用堆叠法将搜索到的网络架构进行多模型的融合,从而获取精准的肺结节良恶性分类预测结果。实验结果表明,在肺结节分类常用数据集LIDC-IDRI上,所提算法与最新肺结节分类算法相比具有较好的分类性能和较快的收敛,且所提算法的特异性和精确率分别达到95.37%和93.42%,能够实现良恶性肺结节的准确分类。     

Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques

A computer-aided diagnostic (CAD) system for effective and accurate pulmonary nodule detection is required to detect the nodules at early stage. This paper proposed a novel technique to detect and classify pulmonary nodules based on statistical features for intensity values using support vector machine (SVM). The significance of the proposed technique is, it uses the nodules features in 2D & 3D and also SVM for the classification that is good to classify the nodules extracted from the image. The lung volume is extracted from Lung CT using thresholding, background removal, hole-filling and contour correction of lung lobe. The candidate nodules are extracted and pruned using the rules based on ground truth of nodules. The statistical features for intensity values are extracted from candidate nodules. The nodule data are up-samples to reduce the biasness. The classifier SVM is trained using data samples. The efficiency of proposed CAD system is tested and evaluated using Lung Image Consortium Database (LIDC) that is standard data-set used in CAD Systems for Lungs Nodule classification. The results obtained from proposed CAD system are good as compare to previous CAD systems. The sensitivity of 96.31% is achieved in the proposed CAD system.     

Hessian based approaches for 3D lung nodule segmentation

In the design of computer-aided diagnosis systems for lung cancer diagnosis, an appropriate and accurate segmentation of the pulmonary nodules in computerized tomography (CT) is one of the most relevant and difficult tasks. An accurate segmentation is crucial for the posterior measurement of nodule characteristics and for lung cancer diagnosis.This paper proposes different approaches that use Hessian-based strategies for lung nodule segmentation in chest CT scans. We propose a multiscale segmentation process that uses the central medialness adaptive principle, a Hessian-based strategy that was originally formulated for tubular extraction but it also provides good segmentation results in blob-like structures as is the case of lung nodules. We compared this proposal with a well established Hessian-based strategy that calculates the Shape Index (SI) and Curvedness (CV). We adapted the SI and CV approach for multiscale nodule segmentation. Moreover, we propose the combination of both strategies by combining the results, in order to take benefit of the advantages of both strategies.Different cases with pulmonary nodules from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were taken and used to analyze and validate the approaches. The chest CT images present a large variability in nodule characteristics and image conditions. Our proposals provide an accurate lung nodule segmentation, similar to radiologists performance. Our Hessian-based approaches were validated with 569 solid and mostly solid nodules demonstrating that these novel strategies have good results when compared with the radiologists segmentations, providing accurate pulmonary nodule volumes for posterior characterization and appropriate diagnosis.     

Zernike-CNNs for image preprocessing and classification in printed register detection

In the register detection of printing field, a new approach based on Zernike-CNNs is proposed. The edge feature of image is extracted by Zernike moments (ZMs), and a recursive algorithm of ZMs called Kintner method is derived. An improved convolutional neural networks (CNNs) are investigated to improve the accuracy of classification. Based on the classic convolutional neural network (CNN), the improved CNNs adopt parallel CNN to enhance local features, and adopt auxiliary classification part to modify classification layer weights. A printed image is trained with 7?×?400 samples and tested with 7?×?100 samples, and then the method in this paper is compared with other methods. In image processing, Zernike is compared with Sobel method, Laplacian of Gaussian (LoG) method, Smallest Univalue Segment Assimilating Nucleus (SUSAN) method, Finite Impusle Response (FIR) method, Multi-scale Morphological Gradient (MMG) method. In image classification, improved CNNs are compared with classical CNN. The experimental results show that Zernike-CNNs have the best performance, the mean square error (MSE) of the training samples reaches 0.0143, and the detection accuracy of training samples and test samples reached 91.43% and 94.85% respectively. The experiments reveal that Zernike-CNNs are a feasible approach for register detection.

     

A fast BP networks with dynamic sample selection for handwritten recognition

Training time of traditional multilayer perceptrons (MLPs) using back-propagation algorithm rises seriously with the problem scale. For multi-class problems, the convergence ratio is very low for training MLPs. The huge time-consuming and low convergence ratio greatly restricts the applications of MLPs on problems with tens and thousands of samples. To deal with these disadvantages, this paper proposes a fast BP network with dynamic sample selection (BPNDSS) method which can dynamically select the samples containing more contribution to the variation of the decision boundary for training after each iteration epoch. The proposed BPNDSS can significantly increase the training speed by only selecting a small subset of the whole samples. Moreover, two kinds of modular single-hidden-layer approaches are adopted to decompose a multi-class problem into multiple binary-class sub-problems, which result in the high rate of convergence. The experiments on Letter and MNIST handwritten recognition database show the effectiveness and the efficiency of BPNDSS. Moreover, BPNDSS results in comparable classification performance to the convolutional neural networks (CNNs), support vector machine, Adaboost, C4.5, and nearest neighbour algorithms. To further demonstrate the training speed improvement of the dynamic sample selection approach on large-scale datasets, we modify CNN to propose a dynamic sample selection CNN (DynCNN). Experiments on Image-Net dataset illustrate that DynCNN can result in similar performance to CNN, but consume less training time.     

基于分布先验的半监督FCM的肺结节分类

肺结节的良恶性分类对于肺癌的早期发现及诊断具有重要意义。然而实际应用中,标记的图像数量较少,且获取标记将耗费大量的人力,在这种情况下,使用半监督学习算法是有效提高分类性能的一个思路。作为一种经典的半监督学习算法,传统的半监督FCM在未标记样本与标记样本分布不平衡情况下不能充分利用标记信息。针对此问题,本文提出了一种基于分布先验的半监督FCM算法。首先计算样本的先验分布概率,基于获得的先验概率,给样本赋予权重,并将其融入到半监督FCM聚类中,从而强化少量的标记样本在聚类过程中的指导作用。文中在LIDC数据库上进行了相应的实验,实验结果证明,相比较传统的半监督FCM算法,提出的算法能够取得更好的肺结节分类性能。