激光与光电子学进展, 2019, 56 (8): 081001, 网络出版: 2019-07-26   

一种基于增强卷积神经网络的病理图像诊断算法 下载: 1240次

Algorithm for Pathological Image Diagnosis Based on Boosting Convolutional Neural Network
作者单位
天津大学电气自动化与信息工程学院, 天津 300072
摘要
自动病理图像诊断是医学图像分析的一个重要课题,实现精确诊断的前提是提取健康与患病组织的形态特征。本文以深度神经网络为工具,提出一种增强卷积网络模型,通过训练一对互补的卷积神经网络,以优化病理图像诊断准确率。由于病理图像获取成本较高,为降低因训练样本数量有限造成的过拟合风险,算法首先训练基本网络,来估计病理图像中各局部组织患病的概率,之后训练另一异构网络,对基本网络的判决结果进行修正。实验在宾夕法尼亚州立大学动物诊断实验室提供的肾、肺、脾组织数据集与淋巴结癌症转移检测数据集上展开,实验结果表明所设计模型在不同器官的病理图像上均表现出较高的诊断准确率。
Abstract
Automatic pathological image diagnosis is an important topic in medical image analysis, and the prerequisite for an accurate pathological image diagnosis is to capture the distinctive morphological features of normal and abnormal tissues. With a deep neural network as a tool, a boosting convolutional neural network is proposed, in which a pair of complementary networks is trained to optimize the accuracy of a pathological image diagnosis. To reduce the risk of over-fitting caused by the scarce training examples due to the high cost of obtaining pathological images, in the proposed algorithm, a basic classifier is first trained to estimate the probabilities of local tissues being abnormal, and then another heterogeneous network is trained to correct the predictions made by the basic one. The extensive experiments are carried out on the Cancer Metastasis Detection on Lymph Node dataset and the Animal Diagnostics Lab dataset provided by Pennsylvania State University which contains the pathological images of three organs (i.e., kidney, lung and spleen). The experimental results show that the proposed model can be used to achieve a high accuracy on the pathological images of different organs.

孟婷, 刘宇航, 张凯昱. 一种基于增强卷积神经网络的病理图像诊断算法[J]. 激光与光电子学进展, 2019, 56(8): 081001. Ting Meng, Yuhang Liu, Kaiyu Zhang. Algorithm for Pathological Image Diagnosis Based on Boosting Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(8): 081001.

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