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| http://dx.doi.org/10.1016/j.biopsych.2022.09.025 | DOI Listing |
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FL-W3S: Cross-domain federated learning for weakly supervised semantic segmentation of white blood cells.
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Department of Computer Science and Artificial Intelligence, University of Udine, 33100, Italy.
Background: Segmentation models for clinical data experience severe performance degradation when trained on a single client from one domain and distributed to other clients from different domain. Federated Learning (FL) provides a solution by enabling multi-party collaborative learning without compromising the confidentiality of clients' private data.
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Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.
PLoS One
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Faculty of Science and Engineering, School of Computer Science, University of Hull, Hull, United Kingdom.
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View Article and Find Full Text PDFFactors associated with uptake and acceptability of cervical cancer screening among female sex workers in Northeastern Uganda: A cross-sectional study.
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Department of Internal Medicine, Faculty of Medicine, Gulu University, Gulu, Uganda.
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View Article and Find Full Text PDFIncidence and predictors of mortality among HIV positive children on anti-retroviral therapy in the selected health facilities of West Wollega Zone, Western Ethiopia: Retrospective cohort study.
PLoS One
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Departments of Public Health, Institute of Health Sciences, Wollega University, Ethiopia.
Introduction: The mortality rate among Human immunodeficiency Virus (HIV) who have started antiretroviral therapy (ART) continues to be increased in resource-limited countries, despite a decline in developed nations. Furthermore, research within this age group is limited and has not previously been conducted in the study area. Consequently, this study aimed to determine the incidence of mortality and its predictors among HIV-positive children who have been receiving ART at public health facilities in West Wollega.
View Article and Find Full Text PDFGenerative adversarial local density-based unsupervised anomaly detection.
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School of Information Science and Engineering, Xinjiang University, Urumqi, China.
Anomaly detection is crucial in areas such as financial fraud identification, cybersecurity defense, and health monitoring, as it directly affects the accuracy and security of decision-making. Existing generative adversarial nets (GANs)-based anomaly detection methods overlook the importance of local density, limiting their effectiveness in detecting anomaly objects in complex data distributions. To address this challenge, we introduce a generative adversarial local density-based anomaly detection (GALD) method, which combines the data distribution modeling capabilities of GANs with local synthetic density analysis.
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