Machine Learning Theory and Applications for Healthcare

Call for Papers Machine learning has evolved from pattern recognition and computational learning theory in artificial intelligence, exploring the construction and study of algorithms that learn from data and make predictions. Machine learning is increasingly applied to healthcare, including medical image segmentation, image registration, multimodal image fusion, computer-aided diagnosis, image-guided therapy, image annotation, and image database retrieval, where failure could be fatal. The purpose of this special issue is to advance scientific research in the broad field of machine learning in healthcare, with focuses on theory, applications, recent challenges, and cutting-edge techniques. Machine learning techniques (e.g., support vector machines, statistical or mathematical methods, extreme learning machines, deep learning, artificial neural networks, evolutionary algorithms, multiobjective metaheuristics, learning through fuzzy logic, cooperative learning, multiagent learning, and planning) with their theory and applications…

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