An Automated Method with Attention Network for Cervical Cancer Scanning

Cervical cancer is a major threat to women’s health and there is a huge population suffering from it in the world. Colposcopy screening is one of the important methods for early diagnosis of cervical cancer. In this paper, we propose a method based on deep learning for colposcopy images recognition, which could be used for early screening of cervical cancer. The method is mainly composed of two parts, the segmentation of the diseased tissue in the colposcopy image and the classification of the image. In our method, the U-Net is used to extract the ROI of images and a deep convolutional neural network is designed to extract features for classification of the ROI. In addition, we introduce the spatial attention mechanism to make the neural network pay more attention to the diseased tissue in images. Experiments demonstrate that the proposed method has a good performance on the colposcopy images, and even achieve nearly test accuracy of 68.03%, which is better than others by \(\sim \)6%.

[1]  David Levitz,et al.  Characterization of cervigram image sharpness using multiple self-referenced measurements and random forest classifiers , 2018, BiOS.

[2]  F. Talamantes,et al.  Sensitivity of the cervical transformation zone to estrogen-induced squamous carcinogenesis. , 2000, Cancer research.

[3]  Yi-Ping Phoebe Chen,et al.  Image based computer aided diagnosis system for cancer detection , 2015, Expert Syst. Appl..

[4]  Keerthana Prasad,et al.  Detection of Specular Reflection and Segmentation of Cervix Region in Uterine Cervix Images for Cervical Cancer Screening , 2017 .

[5]  Diyi Yang,et al.  Hierarchical Attention Networks for Document Classification , 2016, NAACL.

[6]  Jia Gu,et al.  Automated image analysis of uterine cervical images , 2007, SPIE Medical Imaging.

[7]  Sameer Antani,et al.  Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology Images. , 2017, Procedia Computer Science.

[8]  Guanglu Sun,et al.  Cervical Cancer Diagnosis based on Random Forest , 2017 .

[9]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[10]  Takeo Ishigaki,et al.  Apparent diffusion coefficient in cervical cancer of the uterus: comparison with the normal uterine cervix , 2004, European Radiology.

[11]  Abhishek Das,et al.  A novel humanitarian technology for early detection of cervical neoplasia: ROI extraction and SR detection , 2017, 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC).

[12]  M. Schiffman,et al.  American Cancer Society, American Society for Colposcopy and Cervical Pathology, and American Society for Clinical Pathology screening guidelines for the prevention and early detection of cervical cancer , 2012, CA: a cancer journal for clinicians.

[13]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Qiang Ji,et al.  Classifying cervix tissue patterns with texture analysis , 2000, Pattern Recognit..

[15]  Sunanda Mitra,et al.  Classification of Cervix Lesions Using Filter Bank-Based Texture Mode , 2006, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06).

[16]  Qi Wu,et al.  Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning , 2017, ArXiv.

[17]  Kai Zhang,et al.  Deep learning for image-based cancer detection and diagnosis - A survey , 2018, Pattern Recognit..

[18]  Shiri Gordon,et al.  Content analysis of uterine cervix images: initial steps toward content based indexing and retrieval of cervigrams , 2006, SPIE Medical Imaging.

[19]  Nicolas Wentzensen,et al.  ASCCP Colposcopy Standards: Role of Colposcopy, Benefits, Potential Harms, and Terminology for Colposcopic Practice , 2017, Journal of lower genital tract disease.

[20]  T. Kessler,et al.  Cervical Cancer: Prevention and Early Detection. , 2017, Seminars in oncology nursing.

[21]  Gang Sun,et al.  Squeeze-and-Excitation Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[22]  C. Haie-meder,et al.  Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners , 2017, Oncotarget.

[23]  Hayit Greenspan,et al.  Automatic detection of specular reflections in uterine cervix images , 2006, SPIE Medical Imaging.

[24]  Alexandr A. Motyko,et al.  Automated image analysis in multispectral system for cervical cancer diagnostic , 2017, 2017 20th Conference of Open Innovations Association (FRUCT).

[25]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[26]  I. Hagemann,et al.  Utility and Reproducibility of the International Federation for Cervical Pathology and Colposcopy Classification of Transformation Zones in Daily Practice: A Multicenter Study of the German Colposcopy Network , 2015, Journal of lower genital tract disease.

[27]  Jose Jeronimo,et al.  Comparative performance analysis of cervix ROI extraction and specular reflection removal algorithms for uterine cervix image analysis , 2007, SPIE Medical Imaging.

[28]  Guillermo Sapiro,et al.  Image processing and machine learning techniques to automate diagnosis of Lugol's iodine cervigrams for a low-cost point-of-care digital colposcope , 2018, BiOS.

[29]  Jaime S. Cardoso,et al.  Supervised deep learning embeddings for the prediction of cervical cancer diagnosis , 2018, PeerJ Comput. Sci..

[30]  C. Mathers,et al.  GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet]. Lyon, France: International Agency for Research on Cancer , 2013 .