计算机科学 ›› 2019, Vol. 46 ›› Issue (5): 247-253.doi: 10.11896/j.issn.1002-137X.2019.05.038
所属专题: 医学图像
胡海根1, 孔祥勇1, 周乾伟1, 管秋1, 陈胜勇1,2
HU Hai-gen1, KONG Xiang-yong1, ZHOU Qian-wei1, GUAN Qiu1, CHEN Sheng-yong1,2
摘要: 针对黑色素瘤分类识别任务中存在对比度低、肉眼难以区分、信息干扰大、数据量偏少以及数据不均衡等诸多问题,文中提出了一种基于掩盖的数据增强与深度卷积残差网络相结合的集成分类方法。首先根据皮肤病图像的特点,在前人数据增强研究的基础上,提出了两种基于掩盖训练图像部分区域的数据增强方式;其次以这两种数据增强方式为基础,采用深度卷积残差50层(ResNet-50)网络进行特征提取;然后以提取到的特征来构建两个具有一定差异性的分类结构模型,并对其进行集成;最后以国际皮肤影像协作组织(ISIC)2016挑战赛所公布的皮肤病图像数据集为对象,通过一系列实验对提出的方法进行了验证测试。实验结果表明,所提出的集成分类结构模型能弥补单一卷积残差网络在黑色素瘤分类任务中的缺陷,该模型能够在训练样本较少的皮肤病数据集上取得较好的分类结果,多项评估指标均优于ISIC2016挑战赛的前5名。
中图分类号:
[1]JERANT A,JOHNSON J,SHERIDAN C,et al.Early detection and treatment of skin cancer [J].American Family Physician,2000,62(2):357-386. [2]SIEGEL R L,MILLER K D,JEMAL A.Cancer statistics,2015 [J].CA:A Cancer Journal for Clinicians,2015,65(1):5-29. [3]XIE F Y,FAN H,YANG L,et al.Melanoma Classification on Dermoscopy Images using a Neural Network Ensemble Model [J].IEEE Transactions on Medical Imaging,2016,36(3):849-858. [4]SI L,GUO J.Interpretation of clinical practice guidelines formanagement of melanoma in China(new version of 2011) [J].Chinese Clinical Oncology,2012,17(2):172-173.(in Chinese)斯璐,郭军.新版中国黑素瘤诊治指南解读[J].临床肿瘤学杂志,2012,17(2):172-173. [5]BINDER M,SCHWARZ M,WINKLER A,et al.Epilumines-cence microscopy:a useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists [J].Archives of Dermatology (ARCH DERMATOL),1995,131(3):286-291. [6]GUO L L,DING S F.Research Progress in Deep Learning [J].Computer Science,2015,42(5):28-33.(in Chinese)郭丽丽,丁世飞.深度学习研究进展[J].计算机科学,2015,42(5):28-33. [7]HU H,LUO C,GUAN Q,et al.A fast online multivariableidentification method for greenhouse environment control problems [J].Neurocomputing,2018,312:63-73. [8]HU H,GUAN Q,CHEN S,et al.Detection and Recognition for Life State of Cell Cancer Using Two-Stage Cascade CNNs [J].IEEE/ACM Transactions on Computational Biology and Bioinformatics,2017:1. [9]WANG X,SHRIVASTAVA A,GUPTA A.A-fast-rcnn:Hardpositive generation via adversary for object detection[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2017:3039-3048. [10]ZHONG Z,ZHENG L,KANG G,et al.Random Erasing Data Augmentation [J].arXiv Preprint arXiv:1708.04896,2017. [11]SINGH K K,LEE Y J.Hide-and-seek:Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization [J].arXiv preprint arXiv:1704.04232,2017. [12]STANLEY R J,STOECKER W V,MOSS R H.A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images [J].Skin Research and Technology (Skin Res Tech),2007,13(1):62-72. [13]CHENG Y,SWAMISAI R,UMBAUGH S E,et al.Skin lesion classification using relative color features [J].Skin Research and Technology (Skin Res Tech),2008,14(1):53-64. [14]RAHIL G,ALDEEN M,BAILEY J.Computer-Aided Diagnosis of Melanoma Using Border-and Wavelet-Based Texture Analysis [J].IEEE transactions on information technology in biomedicine:A publication of the IEEE Engineering in Medicine and Biology Society,2012,16(6):1239-1252. [15]BALLERINI L,FISHER R B,ALDRIDGE B,et al.A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions[M]∥Color Medical Image Analysis.2013. [16]ALFED N,KHELIFI F,BOURIDANE A.Improving a bag of words approach for skin cancer detection in dermoscopic images[C]∥Proceedings of IEEE International Conference on Decision and Information Technologies (CoDIT).2016:228-232. [17]PREMALADHA J,RAVICHANDRAN K S.Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms [J].Journal of Medical Systems,2016,40(4):1-12. [18]POMPONIU V,NEJATI H,CHEUNG N M.Deepmole:Deepneural networks for skin mole lesion classification[C]∥Proceedings of IEEE International Conference on Image Processing (ICIP).2016:2623-2627. [19]ESTEVA A,KUPREL B,NOVOA R A,et al.Dermatologist-level classification of skin cancer with deep neural networks [J].Nature,2017,542(7639):115-118. [20]YU L,CHEN H,DOU Q,et al.Automated melanoma recognition in dermoscopy images via very deep residual networks [J].IEEE Transactions on Medical Imaging,2017,36(4):994-1004. [21]YI X,WALIA E,BABYN P.Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification [J].arXiv Preprint arXiv:1804.03700,2018. [22]HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Las Vegas:IEEE Press,2016:770-778. [23]SVETNIK V,WANG T,TONG C,et al.Boosting:an ensemble learning tool for compound classification and QSAR modeling [J].Journal of Chemical Information & Modeling,2005,45(3):786-799. [24]FANG M.Study of integration method for multiple classifiers onensemble learning [J].Journal of Systems Engineering and Electronics,2006,28(11):1759-1761.(in Chinese)方敏.集成学习的多分类器动态融合方法研究[J].系统工程与电子技术,2006,28(11):1759-1761. [25]GUTMAN D,CODELLA N C F,CELEBI E,et al.Skin Lesion Analysis toward Melanoma Detection:A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016,hosted by the International Skin Imaging Collaboration (ISIC) [J].ar-Xiv Preprint arXiv:1605.0139,2016. [25]GUTMAN D,CODELLA N C F,CELEBI E,et al.Skin Lesion Analysis toward Melanoma Detection:A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016,hosted by the International Skin Imaging Collaboration (ISIC)[J].arXiv Preprint arXiv:1605.0139,2016. [26]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep co olutional neural networks[C]∥Proceedings of the Advances in Neural Information Processing Systems.South Lake Tahoe,US:2012:1097-1105. [27]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]∥Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2015:1-9. |
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