基于卷积神经网络的医学图像癌变识别研究

发布时间：2018-01-30 18:13

本文关键词： 医学图像识别卷积神经网络特征学习多任务学习　出处：《中国科学技术大学》2017年博士论文　论文类型：学位论文

【摘要】：医学成像技术与相关图像识别算法的快速发展反映了人们对医学信息获取的强烈需求。医学图像能够提供丰富的信息,在医学诊断中的作用日益凸显。计算机识别算法能够克服人工识别易受认知能力、主观经验、疲劳程度影响的不足,有效提高识别的准确率和稳定性,减少误诊和漏诊,对病情诊断、病理分析及治疗方案的选取有重大意义。卷积神经网络能够提供基于学习的特征表示,基于卷积神经网络模型的图像分类、检测、分割算法在医学图像识别上有广泛应用。本文的研究内容的是基于卷积神经网络的图像分类和语义分割算法,应用于医学图像中癌变目标识别。主要工作包括:1)基于CNN-SVM的微血管分型识别算法研究。微血管分型与癌症发展密切相关,分型识别是食道癌诊断与治疗的前提。CNN由数据驱动,相比手工设计特征更加适合复杂多变的微血管图像。本文在样本量相对较少的情况下,设计了一个以CNN-SVM为核心模型的微血管分型识别系统,对一系列的数据扩增技术进行了研究,逐步地提升系统对缩放、旋转图像预测的鲁棒性;在分类器提升方面,引入SVM替换softmax增强了系统的泛化能力。对比广泛使用手工设计特征,CNN彰显了优越的特征表达能力。2)基于多约束FCN的微血管分型语义分割算法研究。本文提出采用语义分割算法对微血管分型进行识别。针对不完全标注问题,结合人工知识,从标注信息中挖掘出感兴趣区域信息,构建了一个基于多约束FCN的语义分割系统。感兴趣区域标签包含了人工积累的经验,该系统采用多任务学习框架,利用多种标签提升了编码器类间区分能力,从而提高了 FCN网络的分割性能。3)基于联合学习FCN的细胞图像语义分割算法研究。显微镜下癌变细胞识别是病理检查的主要内容,也是癌症确诊的关键。对癌细胞区域进行精细划分十分困难。本文采用语义分割算法对癌变区域进行识别。根据多任务学习思想,设计了分类任务,提出CNN与FCN的联合学习方法。通过对分类任务的探索,完成了模型优化,并对额外数据集的价值进行了验证。在多任务学习的框架下,通过提升分类任务性能间接地改善了分割任务的性能。综上所述,本文从模型和数据两个方面来提升卷积神经网络的特征表达能力。模型方面的研究工作包括设计网络结构、引入SVM、采用BN规范化和探究基于梯度下降的优化算法;数据方面的研究工作包括探索数据集扩增技术,设计感兴趣区域标签,利用额外有价值数据。实验表明这些改进能提升识别性能。
[Abstract]:The rapid development of medical imaging technology and image recognition algorithm reflects the strong demand for medical information. Medical image can provide abundant information in medical diagnosis is playing an increasingly prominent role. The computer recognition algorithm can overcome the artificial recognition by cognitive ability, subjective experience, lack of fatigue effect, effectively improve the accuracy the recognition rate and stability, reduce misdiagnosis and missed diagnosis, the diagnosis, pathological analysis and selection of treatment plan is of great significance. The convolution neural network can provide learning based on the characteristics of representation, image classification, based on the model of convolutional neural network detection, segmentation algorithm has been widely used in medical image recognition on the research contents of this paper. The image classification and semantic segmentation algorithm based on convolutional neural network is applied to target recognition, canceration of the medical image. The main work includes: 1) base Study on the recognition algorithm is divided in CNN-SVM. Microvessel microvessel typing and cancer is closely related to the development, type recognition is the premise of.CNN for the diagnosis and treatment of esophageal cancer by data driven, manual design features compared to more suitable for micro vascular image is complex. This paper is relatively less in the sample volume, design a with CNN-SVM as the core model of the micro vascular pattern recognition system, amplification of a series of data, and gradually improve the system robustness zoom, rotate the image prediction; in the classifier upgrade, the introduction of SVM to replace softmax to enhance the generalization ability of the system. Compared with widely used manual design features, highlighting the CNN the superior feature representation ability of.2) microvascular multi constrained FCN type segmentation algorithm based on semantic study. This paper adopts semantic segmentation algorithm for micro vascular pattern recognition. At the end The annotation problem, combined with artificial knowledge, from the annotation information mining region of interest information, constructs a semantic segmentation system based on multi constrained FCN. Region of interest label contains the artificial experience, the system adopts multi task learning framework, the label to enhance the ability to distinguish between types of encoder using a variety, and to improve the segmentation performance of.3 FCN network) segmentation algorithm based on semantic association learning cell image based on FCN. Identification of cancerous cells under the microscope is the main content of pathological examination, is also a key cancer diagnosis. It is very difficult to fine division of cancer cell region. This paper uses the semantic segmentation algorithm is used to identify the cancerous area. According to multi task the thought of learning, design the classification task, proposed the joint learning method of CNN and FCN. Through the exploration of the classification task, complete the optimization model, and the additional data set The value is verified. In the framework of multi task learning, by improving the classification task performance indirectly improves the performance of the segmentation task. To sum up, this paper to improve the characteristics of convolutional neural network from two aspects of model and data expression ability. Research model including design of network structure, the introduction of SVM, using the BN specification and explore the optimization algorithm based on gradient descent; research data include exploration data set amplification technology, design ROI label, using additional valuable data. Experiments show that these improvements can improve the recognition performance.

【学位授予单位】：中国科学技术大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：TP391.41;TP183

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