CASIA OpenIR  > 毕业生  > 博士学位论文
Thesis Advisor田捷
Degree Grantor中国科学院大学
Place of Conferral北京
Keyword卷积神经网络 良恶性 多尺度特征 迁移学习
1. 针对传统肺结节良恶性诊断算法中面临的肺结节分割困难和特征提取繁琐这两大问题,本论文提出了一种基于多尺度结节图像块的卷积神经网络算法。该算法以结节为中心,提取不同尺度的结节图像块,通过卷积神经网络提取不同尺度下的高度区分性特征,对肺结节恶性可疑度分类具有较好效果;
2. 针对高分辨率下肺结节图像的多尺度特征提取耗时这一问题,本论文提出了一种基于特征空间多区域池化的卷积神经网络算法。该算法对卷积特征的不同区域采用不同次数的池化操作,从单一尺度的卷积神经网络中提取多尺度特征,在对肺结节恶性可疑度分类达到较高正确率的同时,降低了计算量;
3. 针对医学图像分析领域中带有诊断信息的数据量较少,而卷积神经网络对数据量要求较大这一问题,本论文提出了一种基于迁移学习和多示例学习的病人级别肺癌检测算法,实现了对较大数据集的知识迁移以及与确诊数据集的知识融合,对病人级别肺癌检测具有较高的正确率。
Other Abstract
Lung cancer is an aggressive cancer that risks people's health and it has a low long-term survival rate. Therefore, early diagnosis of lung cancer is important for cancer treatment. However, diagnosis of nodule malignancy based on biopsy or surgery is invasive which causes patients great pain when compared to non-invasive methods.
In recent years, computer-aided diagnosis has developed rapidly as a non-invasive method for diagnosis. Through analyzing lesion images, the computer-aided diagnosis system yields its report for clinical decision support. In the scenario of lung nodule malignancy classification, the traditional computer-aided diagnosis system faces two major challenges: the development of robust nodule segmentation method and effective malignancy relevant feature extraction. These challenges should be addressed when developing new computer-aided diagnosis algorithms.  As a powerful image analysis tool, the convolutional neural network (CNN), has been widely used in the field of image recognition and classification. There are several advantages using CNNs for image related analysis: a) the network is an end-to-end machine learning architecture where the input is the raw image and the output is the image label; b) due to the supervised learning scheme, discriminative features can be automatically learned. Therefore, in the scenario of lung nodule malignancy classification, CNNs can solve the two major challenges mentioned above. 
In this dissertation, we propose CNN based methods to perform lung nodule malignancy classification. Specifically, we propose a multi-scale CNN and a multi-crop CNN for lung nodule malignancy suspiciousness classification. We also investigate nodule malignancy suspiciousness knowledge transfer and adaptation in patient-level lung cancer detection. Our contributions are as follows:
1. Traditional lung nodule diagnosis systems face the challenges from nodule segmentation and feature extraction. In this dissertation, we propose to use a multi-scale CNN for lung nodule malignancy suspiciousness classification. The input images are multi-scale lung nodule patches. Features under each scale are extracted by a shared CNN and then are concatenated as the multi-scale features. We demonstrate that the proposed method can achieve high classification accuracy.
2. Multi-scale feature extraction is time-consuming when the nodule patch is large. In this dissertation, we propose a multi-crop CNN which crops different regions from convolutional feature maps and then applies max-pooling different times. Multi-scale features are extracted from a single scale network which reduces the computation cost. We demonstrate that the proposed method can also achieve high classification accuracy.
3. The performance of CNNs depends on the size of the dataset while diagnosis-definite medical datasets are usually very small. In this dissertation, we propose an integrated framework based on transfer learning and multiple instance learning to detect patient-level lung cancer.  The transferable knowledge is learnt from a large dataset and is then integrated with the knowledge from the diagnosis-definite dataset. We demonstrate that the proposed method performances well on patient-level lung cancer detection. 
Subject Area医学图像处理
Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
沈伟. 基于卷积神经网络的肺结节良恶性分类算法研究[D]. 北京. 中国科学院大学,2016.
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