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: 1244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
引用该论文

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.

参考文献

[1] 何雪英, 韩忠义, 魏本征. 基于深度学习的乳腺癌病理图像自动分类[J]. 计算机工程与应用, 2018, 54(12): 121-125.

    He X Y, Han Z Y, Wei B Z. Breast cancer histopathological image auto-classification using deep learning[J]. Computer Engineering and Applications, 2018, 54(12): 121-125.

[2] 郑群花, 段慧芳, 沈尧, 等. 基于卷积神经网络和迁移学习的乳腺癌病理图像分类[J]. 计算机应用与软件, 2018, 35(7): 237-242.

    Zheng Q H, Duan H F, Shen Y, et al. Breast cancer histological image classification based on convolutional neural network and transfer learning[J]. Computer Applications and Software, 2018, 35(7): 237-242.

[3] Gurcan M N, Boucheron L E, Can A. et al. Histopathological image analysis: A review[J]. IEEE Reviews in Biomedical Engineering, 2009, 2: 147-171.

[4] GianniniV, RosatiS, CastagneriC, et al. Radiomics for pretreatment prediction of pathological response to neoadjuvant therapy using magnetic resonance imaging: Influence of feature selection[C]∥2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), April 4-7, 2018, Washington, DC, USA. New York: IEEE, 2018: 285- 288.

[5] Alzubaidi AK, Sideseq FB, FaeqA, et al. Computer aided diagnosis in digital pathology application: Review and perspective approach in lung cancer classification[C]∥2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT), March 7-9 ,2017, Baghdad, Iraq. New York: IEEE, 2017: 219- 224.

[6] 郭琳琳, 李岳楠. 基于专家乘积系统的组织病理图像分类算法[J]. 激光与光电子学进展, 2018, 55(2): 021008.

    Guo L L, Li Y N. Histopathological image classification algorithm based on product of experts[J]. Laser & Optoelectronics Progress, 2018, 55(2): 021008.

[7] Vu T H, Mousavi H S, Monga V, et al. Histopathological image classification using discriminative feature-oriented dictionary learning[J]. IEEE Transactions on Medical Imaging, 2016, 35(3): 738-751.

[8] SrinivasU, MousaviH, JeonC, et al. SHIRC: A simultaneous sparsity model for histopathological image representation and classification[C]∥2013 IEEE 10th International Symposium on Biomedical Imaging, April 7-11, 2013, San Francisco, CA, USA. New York: IEEE, 2013: 1118- 1121.

[9] 李振东, 钟勇, 陈蔓, 等. 基于深度特征的快速人脸图像检索方法[J]. 光学学报, 2018, 38(10): 1010004.

    Li Z D, Zhong Y, Chen M, et al. Fast face image retrieval based on depth feature[J]. Acta Optica Sinica, 2018, 38(10): 1010004.

[10] 李素梅, 雷国庆, 范如. 基于双通道卷积神经网络的深度图超分辨率研究[J]. 光学学报, 2018, 38(10): 1010002.

    Li S M, Lei G Q, Fan R. Depth map super-resolution based on two-channel convolutional neural network[J]. Acta Optica Sinica, 2018, 38(10): 1010002.

[11] YonekuraA, KawanakaH, Peasath V B S, et al. Improving the generalization of disease stage classification with deep CNN for glioma histopathological images[C]∥2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), November 13-16, 2017, Kansas City, MO, USA. New York: IEEE, 2017: 1222- 1226.

[12] ShaoW, SunL, Zhang DQ. Deep active learning for nucleus classification in pathology images[C]∥2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), April 4-7, 2018, Washington, DC, USA. New York: IEEE, 2018: 199- 202.

[13] SzegedyC, LiuW, JiaY, et al. Going deeper with convolutions[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 1- 9.

[14] 褚晶辉, 吴泽蕤, 吕卫, 等. 基于迁移学习和深度卷积神经网络的乳腺肿瘤诊断系统[J]. 激光与光电子学进展, 2018, 55(8): 081001.

    Chu J H, Wu Z R, Lü W, et al. Breast cancer diagnosis system based on transfer learning and deep convolutional neural networks[J]. Laser & Optoelectronics Progress, 2018, 55(8): 081001.

[15] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

[16] Lin HJ, ChenH, DouQ, et al. ScanNet: a fast and dense scanning framework for metastastic breast cancer detection from whole-slide image[C]∥2018 IEEE Winter Conference on Applications of Computer Vision (WACV), March 12-15, 2018, Lake Tahoe, NV, USA. New York: IEEE, 2018: 539- 546.

[17] SimonyanK, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. ( 2015-09-04)[2018-09-01]. org/abs/1409. 1556. https://arxiv.

[18] Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66.

[19] ChenR, JingY, Jackson H. Identifying metastases in sentinel lymph nodes with deep convolutional neural networks[EB/OL]. ( 2016-08-04)[2018-09-01]. org/abs/1608. 01658. https://arxiv.

[20] WangD, KhoslaA, GargeyaR, et al. Deep learning for identifying metastatic breast cancer[EB/OL]. ( 2016-06-18)[2018-09-01]. org/abs/1606. 05718. https://arxiv.

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.

本文已被 4 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!