目的:超声成像由于其非侵入性,是子宫内膜疾病早期诊断的首选方法,低成本,和实时成像功能。然而,超声图像的准确评估在很大程度上依赖于放射科医生的经验。因此,稳定客观的计算机辅助诊断模型对于辅助放射科医师诊断子宫内膜病变至关重要.
方法:收集衢州市多家医院的经阴道超声图像,浙江省。数据集包括来自734名患者的1875张图像,包括子宫内膜息肉,增生,和癌症。这里,我们提出了一个基于自我监督的子宫内膜疾病分类模型(BSEM),该模型学习联合统一任务(原始任务和自我监督任务),并应用自我提炼技术和集成策略来帮助医生诊断子宫内膜疾病.
结果:使用五次交叉验证评估BSEM的性能。实验结果表明,BSEM模型在各项指标上取得了令人满意的性能,得分为75.1%,87.3%,76.5%,73.4%,准确率为74.1%,曲线下的面积,精度,召回,和F1得分,分别。此外,与基线模型ResNet相比,DenseNet,VGGNet,ConvNeXt,VIT,和CMT,BSEM模型提高了精度,曲线下的面积,精度,召回,F1得分为3.3-7.9%,3.2-7.3%,3.9-8.5%,3.1-8.5%,和3.3-9.0%,分别。
结论:BSEM模型是早期发现超声显示的子宫内膜疾病的辅助诊断工具,有助于放射科医生在筛查癌前子宫内膜病变时准确有效。
OBJECTIVE: 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.
CONCLUSIONS: 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.