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Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms.

, , , и . Medical Imaging: Image Processing, том 11313 из SPIE Proceedings, стр. 113131B. SPIE, (2020)

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Learning Structure Illuminates Black Boxes - An Introduction to Estimation of Distribution Algorithms., , и . Advances in Metaheuristics for Hard Optimization, Springer, (2008)Computationally Intelligent Online Dynamic Vehicle Routing by Explicit Load Prediction in an Evolutionary Algorithm., и . PPSN, том 4193 из Lecture Notes in Computer Science, стр. 312-321. Springer, (2006)Inventory management and the impact of anticipation in evolutionary stochastic online dynamic optimization., и . IEEE Congress on Evolutionary Computation, стр. 268-275. IEEE, (2007)Data variation-aware medical image segmentation., , , и . Medical Imaging: Image Processing, том 12032 из SPIE Proceedings, SPIE, (2022)Model-Based Evolutionary Algorithms., и . GECCO (Companion), стр. 385-412. ACM, (2016)Model-Based Evolutionary Algorithms., и . GECCO (Companion), стр. 93-120. ACM, (2015)Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy., , , , , , , , , и 4 other автор(ы). CoRR, (2020)Learning and exploiting mixed variable dependencies with a model-based EA., , и . CEC, стр. 4382-4389. IEEE, (2016)Efficiency enhancements for evolutionary capacity planning in distribution grids., , , и . GECCO (Companion), стр. 1189-1196. ACM, (2014)More concise and robust linkage learning by filtering and combining linkage hierarchies., и . GECCO, стр. 359-366. ACM, (2013)