The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images.

Journal: Scientific reports
Published Date: Jul 15, 2020

Abstract

Background Deep learning has presented considerable potential and is gaining more importance in computer assisted diagnosis. As the gold standard for pathologically diagnosing cervical intraepithelial lesions and invasive cervical cancer, colposcopy-guided biopsy faces challenges in improving accuracy and efficiency worldwide, especially in developing countries. To ease the heavy burden of cervical cancer screening, it is urgent to establish a scientific, accurate and efficient method for assisting diagnosis and biopsy. Methods The data were collected to establish three deep-learning-based models. For every case, one saline image, one acetic image, one iodine image and the corresponding clinical information, including age, the results of human papillomavirus testing and cytology, type of transformation zone, and pathologic diagnosis, were collected. The dataset was proportionally divided into three subsets including the training set, the test set and the validation set, at a ratio of 8:1:1. The validation set was used to evaluate model performance. After model establishment, an independent dataset of high-definition images was collected to further evaluate the model performance. In addition, the comparison of diagnostic accuracy between colposcopists and models weas performed. Results The sensitivity, specificity and accuracy of the classification model to differentiate negative cases from positive cases were 85.38%, 82.62% and 84.10% respectively, with an AUC of 0.93. The recall and DICE of the segmentation model to segment suspicious lesions in acetic images were 84.73% and 61.64%, with an average accuracy of 95.59%. Furthermore, 84.67% of high-grade lesions were detected by the acetic detection model. Compared to colposcopists, the diagnostic system performed better in ordinary colposcopy images but slightly unsatisfactory in high-definition images. Implications The deep learning-based diagnostic system could help assist colposcopy diagnosis and biopsy for HSILs.

Authors

  • Chunnv Yuan
    Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China.
  • Yeli Yao
    Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
  • Bei Cheng
    Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
  • Yifan Cheng
    Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
  • Ying Li
    School of Information Engineering, Chang'an University, Xi'an 710010, China.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Xuechen Liu
    College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China.
  • Xiaodong Cheng
    Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
  • Xing Xie
    Microsoft Research, China. Electronic address: xing.xie@microsoft.com.
  • Jian Wu
    Department of Medical Technology, Jiangxi Medical College, Shangrao, Jiangxi, China.
  • Xinyu Wang
    Department of Endocrinology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China.
  • Weiguo Lu
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America.

Keywords

External Resources

Popular Topics
Recent Journals