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- Title
A self-supervised classification model for endometrial diseases.
- Authors
Fang, Yun; Wei, Yanmin; Liu, Xiaoying; Qin, Liufeng; Gao, Yunxia; Yu, Zhengjun; Xu, Xia; Cha, Guofen; Zhu, Xuehua; Wang, Xue; Xu, Lijuan; Cao, Lulu; Chen, Xiangrui; Jiang, Haixia; Zhang, Chaozhen; Zhou, Yuwang; Zhu, Jinqi
- Abstract
Purpose: Ultrasound imaging is the preferred method for the early diagnosis of endometrial diseases because of its non-invasive nature, low cost, and real-time imaging features. However, the accurate evaluation of ultrasound images relies heavily on the experience of radiologist. Therefore, a stable and objective computer-aided diagnostic model is crucial to assist radiologists in diagnosing endometrial lesions. Methods: Transvaginal ultrasound images were collected from multiple hospitals in Quzhou city, Zhejiang province. The dataset comprised 1875 images from 734 patients, including cases of endometrial polyps, hyperplasia, and cancer. Here, we proposed a based self-supervised endometrial disease classification model (BSEM) that learns a joint unified task (raw and self-supervised tasks) and applies self-distillation techniques and ensemble strategies to aid doctors in diagnosing endometrial diseases. Results: The performance of BSEM was evaluated using fivefold cross-validation. The experimental results indicated that the BSEM model achieved satisfactory performance across indicators, with scores of 75.1%, 87.3%, 76.5%, 73.4%, and 74.1% for accuracy, area under the curve, precision, recall, and F1 score, respectively. Furthermore, compared to the baseline models ResNet, DenseNet, VGGNet, ConvNeXt, VIT, and CMT, the BSEM model enhanced accuracy, area under the curve, precision, recall, and F1 score in 3.3–7.9%, 3.2–7.3%, 3.9–8.5%, 3.1–8.5%, and 3.3–9.0%, respectively. Conclusion: The BSEM model is an auxiliary diagnostic tool for the early detection of endometrial diseases revealed by ultrasound and helps radiologists to be accurate and efficient while screening for precancerous endometrial lesions.
- Subjects
ZHEJIANG Sheng (China); ENDOMETRIAL diseases; ENDOMETRIAL hyperplasia; EARLY diagnosis; TRANSVAGINAL ultrasonography; ULTRASONIC imaging; NOSOLOGY
- Publication
Journal of Cancer Research & Clinical Oncology, 2023, Vol 149, Issue 20, p17855
- ISSN
0171-5216
- Publication type
Article
- DOI
10.1007/s00432-023-05467-7