首页|New Artificial Intelligence Study Findings Have Been Reported from University of Louisville (Artificial Intelligence-Based Kidney Segmentation With Modified Cyc le-Consistent Generative Adversarial Network and Appearance-Based Shape Prior)
New Artificial Intelligence Study Findings Have Been Reported from University of Louisville (Artificial Intelligence-Based Kidney Segmentation With Modified Cyc le-Consistent Generative Adversarial Network and Appearance-Based Shape Prior)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on ar tificial intelligence. According to news reportingfrom Louisville, Kentucky, by NewsRx journalists, research stated, “This study presents an innovative deeple arning framework for kidney segmentation in magnetic resonance imaging (MRI) dat a.”Our news correspondents obtained a quote from the research from University of Lo uisville: “Theframework integrates both kidney appearance and prior shape infor mation using a residual cycle-consistentgenerative adversarial network (CycleGA N). An appearance-based shape prior model is developed, utilizingiso-circular c ontours generated from the kidney centroid and employing the fast marching level sets methodfor shape extraction. By utilizing the kidney centroid and matching cross-circular iso-circular contours’appearance, the proposed appearance-based shape prior model remains invariant to translation, rotation,and scaling, elim inating the need for alignment. Additionally, a novel weighted loss function, th e H-Loss,is introduced to enhance segmentation performance and prevent overfitt ing. The proposed approach istested on 34 blood-oxygen-level-dependent (BOLD) g rafts from patients in our kidney transplant program,achieving an average dice score of 92%.”
University of LouisvilleLouisvilleKe ntuckyUnited StatesNorth and Central AmericaArtificial IntelligenceEmerg ing TechnologiesMachine Learning
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