Few studies have reported the reproducibility and stability of ultrasound (US) images based
radiomics features obtained from automatic segmentation in oncology. The purpose of this …

Ultrasound (US) imaging has been recognized and widely used as a screening and
diagnostic imaging modality for cervical cancer all over the world. However, few studies …

Background Upon the discovery of ovarian cysts, obstetricians, gynecologists, and
ultrasound examiners must address the common clinical challenge of distinguishing …

Background Epithelial ovarian cancer (EOC) segmentation is an indispensable step in
assessing the extent of disease and guiding the treatment plan that follows. Currently …

Objective This study aimed to evaluate the performance of the deep convolutional neural
network (DCNN) to discriminate between benign, borderline, and malignant serous ovarian …

Endometrial cancer (EC) is the most common gynecological tumor in developed countries,
and preoperative risk stratification is essential for personalized medicine. There have been …

Transvaginal ultrasound (TVUS) is widely used in infertility treatment. The size and shape of
the ovary and follicles must be measured manually for assessing their physiological status …

Purpose More than 80% of patients with ovarian epithelial cancer (OEC) show complete
remission after initial treatment but eventually experience recurrence of the disease. This …

Discrimination of ovarian tumors is necessary for proper treatment. In this study, we
developed a convolutional neural network model with a convolutional autoencoder (CNN …

Purpose Fast and accurate outlining of the organs at risk (OARs) and high-risk clinical tumor
volume (HRCTV) is especially important in high-dose-rate brachytherapy due to the highly …