Email Alert RSS
Volume 14 Issue 1
Jan.  2023
Turn off MathJax
Article Contents
Article Navigation >  Medical Journal of Peking Union Medical College Hospital  > 2023  >  14(1): 159-167
LIU Chang, ZHENG Yuchao, XIE Wenqian, LI Chen, LI Xiaohan. Image Recognition Method of Cervical Adenocarcinoma in Situ Based on Deep Learning[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(1): 159-167. doi: 10.12290/xhyxzz.2022-0109
Citation: LIU Chang, ZHENG Yuchao, XIE Wenqian, LI Chen, LI Xiaohan. Image Recognition Method of Cervical Adenocarcinoma in Situ Based on Deep Learning[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(1): 159-167. doi: 10.12290/xhyxzz.2022-0109
shu

Image Recognition Method of Cervical Adenocarcinoma in Situ Based on Deep Learning

doi: 10.12290/xhyxzz.2022-0109
  • 1.

    Department of Pathology, Shengjing Hospital, China Medical University, Shenyang 110004, China

  • 2.

    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China

More Information
    Abstract
  •   Objective  To construct a pathological image diagnostic model of cervical adenocarcinoma in situ(CAIS) based on deep learning algorithm.  Methods  Pathological tissue sections of CAIS and normal cervical canal and gland sections of chronic cervicitis stored in the Pathology Department of Shengjing Hospital, China Medical University from January 2019 to December 2021 were retrospectively collected. After image collection, they were randomly divided into training set, validation set and test set with a ratio of 4∶3∶3. The data of training set and validation set were used to conduct transfer learning training and parameter debugging for 6 network models, including VGG16, VGG19, Inception V3, Xception, ResNet50 and DenseNet201, and the convolutional neural network binary classification model that could recognize pathological images of CAIS was constructed. The models were combined to build the ensemble learning model. Based on the test set data, the performance of pathological image recognition of single model and ensemble learning model was evaluated. The results were expressed by operation time, accuracy, precision, recall, F1 score and area under the curve(AUC) of receiver operating characteristic.  Results  A total of 104 pathological sections of CAIS and 90 pathological sections of normal cervical duct and gland with chronic cervicitis were selected. A total of 500 pathological images of CAIS and normal cervical duct glands were collected, including 400 images of training set, 300 images of validation set and 300 images of test set, respectively. Among the 6 models, ResNet50 model, with the highest accuracy(87.33%), precision(90.00%), F1 score(86.90%) and AUC(0.87), second highest recall(84.00%) and shorter operation time(2062.04 s), demonstrated the best overall performance; VGG19 model was the second; and Inception V3 and Xception model had the worst performance.Among the 6 kinds of ensemble learning models, ResNet50 and DenseNet201 showed the best overall performance, and their accuracy, precision, recall, F1 score and AUC were 89.67%, 84.67%, 94.07%, 89.12% and 0.90, respectively. VGG19 and ResNet50 ensemble model followed.  Conclusions  It is feasible to construct CAIS pathological image recognition models by deep learning algorithm, among which ResNet50 models has higher overall performance. Ensemble learning can improve the recognition effect on pathological images by single model.
    • FullText(HTML)
  • loading
    • References(25)
  • [1] Cao W, Chen HD, Yu YW, et al. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020[J]. Chin Med J, 2021, 134: 783-791. doi:  10.1097/CM9.0000000000001474
    [2] Baalbergen A, Helmerhorst TJ. Adenocarcinoma in situ of the uterine cervix: a systematic review[J]. Int J Gynecol Cancer, 2014, 24: 1543-1548. doi:  10.1097/IGC.0000000000000260
    [3] Cleveland AA, Gargano JW, Park IU, et al. Cervical adenocarcinoma in situ: human papillomavirus types and incidence trends in five states, 2008—2015[J]. Int J Cancer, 2020, 146: 810-818. doi:  10.1002/ijc.32340
    [4] 刘从容. 宫颈腺上皮病变病理学相关问题及其研究进展[J]. 中华妇幼临床医学杂志, 2016, 12: 2-6. doi:  10.3877/cma.j.issn.1673-5250.2016.01.001

    Liu CR. Pathologic problems and research progress of cervical adenoepithelial lesions[J]. Zhonghua Fuyou Linchuang Yixue Zazhi, 2016, 12: 2-6. doi:  10.3877/cma.j.issn.1673-5250.2016.01.001
    [5] Liu J, Li L, Wang L. Acetowhite region segmentation in uterine cervix images using a registered ratio image[J]. Comput Biol Med, 2018, 93: 47-55. doi:  10.1016/j.compbiomed.2017.12.009
    [6] Xin TL, Chen L, Md Mamunur R, et al. A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches[J]. Artifi Intell Rev, 2022, 55: 4809-4878. doi:  10.1007/s10462-021-10121-0
    [7] Song D, Kim E, Huang X, et al. Multimodal entity coreference for cervical dysplasia diagnosis[J]. IEEE Trans Med Imaging, 2015, 34: 229-245. doi:  10.1109/TMI.2014.2352311
    [8] Asiedu MN, Simhal A, Chaudhary U, et al. Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, Pocket Colposcope[J]. IEEE Trans Biomed Eng, 2019, 66: 2306-2318. doi:  10.1109/TBME.2018.2887208
    [9] Li C, Chen H, Li XY, et al. A review for cervical histopathology image analysis using machine vision approaches[J]. Artifi Intell Rev, 2020, 53: 4821-4862. doi:  10.1007/s10462-020-09808-7
    [10] 庄福振, 罗平, 何清, 等. 迁移学习研究进展[J]. 软件学报, 2015, 26: 26-39. https://www.cnki.com.cn/Article/CJFDTOTAL-RJXB201501003.htm

    Zhuang FZ, Luo P, He Q, et al. Research progress of transfer learning[J]. Ruanjian Xuebao, 2015, 26: 26-39. https://www.cnki.com.cn/Article/CJFDTOTAL-RJXB201501003.htm
    [11] Jun L, Guang YL, Xiang RT, et al. Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction[J]. Comput Biol Med, 2021, 134: 104504. doi:  10.1016/j.compbiomed.2021.104504
    [12] Niu S, Liu M, Liu Y, et al. Distant Domain Transfer Learning for Medical Imaging[J]. IEEE J Biomed Health Inform, 2021, 25: 3784-3793. doi:  10.1109/JBHI.2021.3051470
    [13] 颜悦, 陈丽萌, 李锦涛, 等. 基于深度学习和组织病理图像的癌症分类研究进展[J]. 协和医学杂志, 2021, 12: 742-748. doi:  10.12290/xhyxzz.2021-0452

    Yan Y, Chen LM, Li JT, et al. Progress in cancer classification based on deep learning and histopathological images[J]. Xiehe Yixue Zazhi, 201, 12: 742-748. doi:  10.12290/xhyxzz.2021-0452
    [14] 杨志明, 李亚伟, 杨冰, 等. 融合宫颈细胞领域特征的多流卷积神经网络分类算法[J]. 计算机辅助设计与图形学学报, 2019, 31: 531-540. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201904003.htm

    Yang ZM, Li YW, Yang B, et al. Multi-flow convolutional neural network classification algorithm based on domain features of cervical cells[J]. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao, 2019, 31: 531-540. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201904003.htm
    [15] Qiao ZH, Herve D, Nicolas D, et al. 3-D consistent and robust segmentation of cardiac images by deep learning with spatial propagation[J]. IEEE Trans Med Imaging, 2018, 37: 2137-2148. doi:  10.1109/TMI.2018.2820742
    [16] Yang H, Sun J, Li H, et al. Neural multi-atlas label fusion: Application to cardiac MR images[J]. Med image Anal, 2018, 49: 60-75. doi:  10.1016/j.media.2018.07.009
    [17] Jamaludin A, Kadir T, Zisserman A. SpineNet: automated classification and evidence visualization in spinal MRIs[J]. Med image Anal, 2017, 41: 63-73. doi:  10.1016/j.media.2017.07.002
    [18] Sato M, Horie K, Hara A, et al. Application of deep learning to the classification of images from colposcopy[J]. Oncol lett, 2018, 15: 3518-3523.
    [19] Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition[J]. Comput Sci, 2014. https://doi.org/10.48550/arXiv.1409.1556. doi:  10.48550/arXiv.1409.1556
    [20] Russakovsky O, Deng J, Su H, et al. Imagenet large scale visual recognition challenge[J]. Int J Comput Vision, 2015, 115: 211-252.
    [21] Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.
    [22] Chollet F. Xception: Deep learning with depthwise separable convolutions[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1251-1258.
    [23] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. Proceedings of the IEEE Confer-ence on Computer Vision and Pattern Recognition, 2016: 770-778.
    [24] Huang G, Liu Z, Van DML, et al. Densely connected convolutional networks[C]. Proceedings of the IEEE Confer-ence on Computer VIsion and Pattern Recognition, 2017: 4700-4708.
    [25] Al-Haija QA, Adebanjo A. Breast Cancer Diagnosis in Histopathological Images Using ResNet-50 Convolutional Neural Network[C]. 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). IEEE, 2020.
    • Relative Articles(0)
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(2)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (426) PDF downloads(80) Cited by()
    Proportional views
    Related
    Links: National Health Commission of the People's Republic of China Chinese Academy of Medical Sciences(CAMS) AMLC Check CNKI Database CNKI Term Online Chinese General Practice
    Address：Room 302,Third Floor,Building 7, No. 1 Shuaifuyuan, Wangfujing, Dongcheng District, Beijing 100730, China
    Tel：010-69154261/4262
    E - mail：medj@pumch.cn
    mjpumch@126.com
    Copyright of website：Editorial Office of Medical Journal of Peking Union Medical College Hospital

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return