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PURPOSE Digital Breast Imaging Reporting and Data System (BI-RADS) features extracted from ultrasound images are essential in computer-aided diagnosis, prediction, and prognosis of breast cancer. This study focuses on the reproducibility of quantitative high-throughput BI-RADS features in the presence of variations due to different segmentation results,(More)
Recent studies have demonstrated the effectiveness of proactive resource allocation under the assumption of perfect prediction of the user's future data rate. In this paper, imperfect rate prediction merely based on the context information including large-scale channel gains of users and statistical information of system available resources is considered.(More)
INTRODUCTION In current clinical practice, invasive ductal carcinoma is always screened using medical imaging techniques and diagnosed using immunohistochemistry. Recent studies have illustrated that radiomics approaches provide a comprehensive characterization of entire tumors and can reveal predictive or prognostic associations between the images and(More)
OBJECTIVES This work focused on extracting novel and validated digital high-throughput features to present a detailed and comprehensive description of the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) with the goal of improving the accuracy of ultrasound breast cancer diagnosis. METHODS First, the phase congruency(More)
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