基于宫颈上皮与血管特征的阴道镜图像 深度学习模型探索.

Objective To explore the feasibility of object detection and deep learning model applied on the localization and classification of cervical precancerous lesions based on identifying the epithelial and vascular features in colposcopy images. Methods A total of 28 975 colposcopic images were collected from Mar 2018 to Jul 2019 in the Obstetrics and Gynecology Hospital of Fudan University, including cervical low-grade lesion (5 708 patients), high-grade lesion (2 206 patients) and cervical cancer (514 patients). According to the colposcopy standardized terminology of the International Federation for Cervical Pathology and Colposcopy and American Society for Colposcopy and Cervical Pathology，39 858 valid labels were obtained after pixel-level labeling based on 16 types of cervical epithelial and vascular signs. In order to reduce the error of fine-grained labeling, labels were further classified into three categories: low-grade, high-grade and cancer. Using ResNet101 pre-training network after secondary transfer learning as feature extractor, the models of high-grade lesion object detection and three categories (low-grade, high-grade and cancer) object detection based on Faster-RCNN network structure were constructed respectively. Results Based on the ResNet101 model of ImageNet pre-training, the first transfer learning was performed through the open source colposcopy data of cervical transformation zone classification, and the second transfer learning was carried out based on our own data of lesion classification to obtain the feature extractor. The average recognition accuracy mAP@IOU=0.5 obtained on the high-grade lesion model and the three categories model were 0.82 and 0.67，respectively. Conclusion Using the large sample data of the largest colposcopy center in China, the deep learning model can make a good performance in the detection of cervical precancerous lesions based on the fine labeling of epithelial and vascular features. Although there was still room for improvement on accuracy, these models were shown to be potential for cervical cancer screening, especially on guidance for location. [ABSTRACT FROM AUTHOR]