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An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.
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Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio.
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School of Civil, Environmental and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea. Electronic address:
Algal blooms in freshwater, which are exacerbated by urbanization and climate change, pose significant challenges in the water treatment process. These blooms affect water quality and treatment efficiency. Effective identification of algal proliferation based on the dominant species is important to ensure safe drinking water and a clean water supply.
View Article and Find Full Text PDFPotential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer.
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Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
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View Article and Find Full Text PDFIdentification of Novel Biomarkers for Ischemic Stroke Through Integrated Bioinformatics Analysis and Machine Learning.
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Lanzhou University Second Hospital, The Second Medical College of Lanzhou University, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.
Ischemic stroke leads to permanent damage to the affected brain tissue, with strict time constraints for effective treatment. Predictive biomarkers demonstrate great potential in the clinical diagnosis of ischemic stroke, significantly enhancing the accuracy of early identification, thereby enabling clinicians to intervene promptly and reduce patient disability and mortality rates. Furthermore, the application of predictive biomarkers facilitates the development of personalized treatment plans tailored to the specific conditions of individual patients, optimizing treatment outcomes and improving prognoses.
View Article and Find Full Text PDFZero-Shot Traffic Identification with Attribute and Graph-Based Representations for Edge Computing.
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School of Economics and Management, Beijing Information Science & Technology University, Beijing 100192, China.
With the proliferation of mobile terminals and the rapid growth of network applications, fine-grained traffic identification has become increasingly challenging. Methods based on machine learning and deep learning have achieved remarkable results, but they heavily rely on the distribution of training data, which makes them ineffective in handling unseen samples. In this paper, we propose AG-ZSL, a zero-shot learning framework based on traffic behavior and attribute representations for general encrypted traffic classification.
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