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

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

More Information
  • Corresponding author:

    LI Chen, E-mail: lichen@bmie.neu.edu.cn

    LI Xiaohan, E-mail: li_xiaohan1975@hotmail.com

  • Received Date: March 09, 2022
  • Accepted Date: May 25, 2022
  • Available Online: September 19, 2022
  • Issue Publish Date: January 29, 2023
  •   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.
  • [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.
    [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.
  • Related Articles

    [1]ZHANG Shan, LIU Jie. Interpretation of NCCN Clinical Practice Guidelines for Primary Cutaneous Lymphomas (Version 1.2024) Based on the Current Diagnosis and Treatment Status of China[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(5): 1029-1037. DOI: 10.12290/xhyxzz.2024-0605
    [2]DU Ping, LIU He, AN Zhuoling. "Seminar-CBL-Ideological and Political Education" Innovative Mode in Clinical Pharmacology Teaching[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(2): 466-469. DOI: 10.12290/xhyxzz.2023-0202
    [3]FAN Wanli, HE Dong, ZHANG Shuze, CHEN Gang, ZHAO Bin, CHENG Zhibin. Predictive Value of Albumin-Bilirubin Score Combined with Liver Function Index and CEA for Liver Metastasis of Colorectal Cancer[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(1): 99-108. DOI: 10.12290/xhyxzz.2023-0261
    [4]WANG Changjun, LIN Yan, ZHOU Yidong, MAO Feng, SHEN Songjie, SUN Qiang. The Impact of Dynamic Adaptive Teaching Model on Surgical Education[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(2): 431-436. DOI: 10.12290/xhyxzz.2022-0142
    [5]LIU Yuan, ZHAO Lin. Update and Interpretation of 2022 National Comprehensive Cancer Network Clinical Practice Guidelines for Gastric Cancer[J]. Medical Journal of Peking Union Medical College Hospital, 2022, 13(6): 999-1004. DOI: 10.12290/xhyxzz.2022-0271
    [6]Yan-meng QI, Ye-cheng LIU, Hua-dong ZHU. Predictive Value of Ranson Score for Typing Moderately Severe and Severe Hyperlipidemic Acute Pancreatitis[J]. Medical Journal of Peking Union Medical College Hospital, 2019, 10(5): 489-493. DOI: 10.3969/j.issn.1674-9081.2019.05.011
    [8]Ming-sheng MA, Xü-de ZHANG, Min WEI, Shi-min ZHAO, Zheng-qing QIU. Efficacy of Low Dose Corticosteroid Therapy in Duchenne Muscular Dystrophy[J]. Medical Journal of Peking Union Medical College Hospital, 2014, 5(4): 384-388. DOI: 10.3969/j.issn.1674-9081.2014.04.006
    [9]Jie LIU, Yue-ping ZENG, Chun-xia HE, Qin LONG, Hong-zhong JIN, Qiu-ning SUN. Corticosteroids plus Intravenous Immunoglobulin in the Treatment of 7 Cases with Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis[J]. Medical Journal of Peking Union Medical College Hospital, 2012, 3(4): 381-385. DOI: 10.3969/j.issn.1674-9081.2012.04.004
    [10]Shuai TANG, Jie YI, Yu-guang HUANG. Cardiovascular Responses of Intubation with Shikani Seeing Optical Stylet and Macintosh Laryngoscope[J]. Medical Journal of Peking Union Medical College Hospital, 2012, 3(3): 314-317. DOI: 10.3969/j.issn.1674-9081.2012.03.015
  • Cited by

    Periodical cited type(24)

    1. 肖琦凡,唐彬,张素巧,朱宇清. 翻转课堂结合Seminar教学法在全科规范化培训中的应用及效果评价. 中国毕业后医学教育. 2024(02): 149-152 .
    2. 叶寅寅,杨沿浪. 肾内科教学中采用SHARP反馈法的效果及对教学满意度的影响. 中国医药科学. 2024(04): 63-66 .
    3. 孙艳,李月红,徐慧灵,蔡雪芬. 微视频联合翻转课堂在“生殖医学”教学中的应用. 教育教学论坛. 2024(20): 135-138 .
    4. 王玉红,柳晓明,葛俊,衣少娜,刘晔. PBL联合CBL教学法在肾内科教学中的应用意义. 中国继续医学教育. 2024(12): 56-60 .
    5. 王斌,郭晓会,吕威,曹克利,高志强,杨华,冯国栋,王剑. 小组培养模式在耳鼻咽喉科临床医学博士试点班教学中的效果评估. 中国医药导报. 2024(19): 72-75 .
    6. 刘倩倩,潘珂,黄小华,刘念. “互联网+”背景下的翻转课堂本科教学实践研究——以“医学影像技术学”课程为例. 教育教学论坛. 2024(28): 165-168 .
    7. 余丹,施玲玲,赖颖晖. 翻转课堂联合TBL教学法在血液科见习中的应用效果. 中国继续医学教育. 2024(15): 93-97 .
    8. 陈书英,由丽娜,黄伍奎. 基于“互联网+”的翻转课堂在《介入放射学》教学中的应用. 中国继续医学教育. 2024(18): 46-49 .
    9. 于艾琳,苏晓坤,王娜. 新型教学方法在消化系统教学中的应用. 中国继续医学教育. 2024(21): 94-97 .
    10. 李婧波,何静,张春辉,唐菠,钟韵宏. “互联网+”视角下的翻转课堂在《内分泌学》教学中的应用实践. 中国继续医学教育. 2024(24): 60-64 .
    11. 朱雪萍,崔铭玲,孙文强,耿海峰. 医学教学中翻转课堂模式的初步探讨. 中国优生与遗传杂志. 2023(01): 195-197 .
    12. 何昀,毕杨. PBL教学法在小儿胃肠外科临床教学中对研究生成绩的影响. 中国继续医学教育. 2023(07): 103-107 .
    13. 柯贵宝. 肾内科教学中翻转课堂教学的效果分析. 继续医学教育. 2023(03): 61-64 .
    14. 顾林,赵睿,马振增,郑海伦,刘茹,邓敏. 基于Seminar教学法联合翻转课堂在消化内科系统疾病教学中的应用及效果评价. 中华全科医学. 2023(05): 868-871 .
    15. 火睿,沙慧敏,孙侃,常向云,李军,朱凌云,董玉洁. 混合式教学模式在内分泌教学中的应用. 中国医药科学. 2023(08): 83-86 .
    16. 白洁,王艳青,杨帆,郑轶,王姗. 翻转课堂结合案例教学法在眼科教学中的应用. 继续医学教育. 2023(05): 33-36 .
    17. 陈娟,薛建波,霍江波,徐涛,曹智强,马丽,康晓. 基于微课的翻转课堂在急性胰腺炎教学中的实践. 中国病案. 2023(09): 101-103 .
    18. 陈隽,周一薇,林清,陈宇宁,李秀娟,高海兵. 翻转课堂联合案例教学法在内科学心血管系统教学中的应用. 中国继续医学教育. 2023(18): 32-36 .
    19. 曾亮,朱丽,谢可欣,王建华,袁永丰. 精准医疗时代背景下医学影像学医教融合新模式的创建. 中国临床研究. 2023(10): 1563-1567 .
    20. 刘代江,蒋俊艳,万晓强. 案例式教学联合PBL教学模式在消化内科规培医生中的应用. 现代医院. 2022(06): 953-955+958 .
    21. 王艳,王玲玲. 基于翻转课堂的CBCL教学在妇产科学本科生教学中的应用. 中国医药科学. 2022(16): 61-64 .
    22. 王军凯,许艺兰,胡君梅,郑卓肇. 翻转课堂在放射科住院医师规范化培训教学中的探索. 医学研究杂志. 2022(10): 176-179 .
    23. 张骏艳,陈灵芝,余莉,刘淼,李群. 翻转课堂在感染与免疫整合课程教学中的应用分析. 科技视界. 2022(23): 127-129 .
    24. 张凯,张浦,孙鑫,干耀恺,马辉,赵杰. 基于虚拟数字仿真技术的翻转课堂模式在骨科教学中的应用效果. 中国当代医药. 2022(30): 158-160+164 .

    Other cited types(1)

Catalog

    Article Metrics

    Article views (533) PDF downloads (95) Cited by(25)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return
    x Close Forever Close