Manuscript Topics

With the development of information technology and the improvement of computing speed, hot artificial intelligence (AI) technologies such as machine learning (ML) and deep learning have made continuous breakthroughs and progress. ML has been widely applied for big data processing and analytics, where various optimization problems (about model architecture and hyperparameters, data clustering, and data prediction) are frequently encountered. The automatic design of ML has become an increasingly popular research trend. Evolutionary computation (EC) is commonly used in these scenarios where classical numerical methods fail to find good enough solutions. Evolutionary approaches can be used in all the parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting and network topology), and postprocessing (e.g., decision tree/support vectors pruning and ensemble learning). It is of great interest to investigate the combination of EC and ML in solving large-scale big data analytic problems.

The success of graph-based neural networks (GNN and graph active/reinforcement learning) has promoted graph pattern recognition, graph data mining, image classification, object detection, semantic segmentation, and object classification. In medical scientific research, medical imaging is a technology and processing procedure to obtain internal tissue images in a non-invasive way, which includes medical image processing and medical imaging system. It is widely used in multi-dimensional image, functional imaging, and multi-mode image, such as imaging mechanism, disease prediction, and pattern classification. The informatization of medical industry has produced a large amount of medical data, making the combination of AI technology and medical health become the current research hotspot. Important achievements have been made in the application of AI methods to disease prediction. With the ability of ML to learn data, many chronic diseases can also be estimated by algorithms for early risk, achieving accurate prevention. In some cases, the diagnostic accuracy of AI has reached or even exceeded that of experts in the field, which has extremely high practical application value. Moreover, the innovative achievements of AI and medical science will serve patients better by improving the uneven distribution of medical resources, reducing costs, and improving diagnostic efficiency.

The interdisciplinary research of this topic focuses on the progress of ML, evolutionary algorithms and their applications for medical science, as well as emerging intelligent applications and models in topics of interest, including, but not limited to, health information technology, medical imaging, pattern recognition, and computer vision.

This Special Issue aims to bring together both experts and newcomers from either academia or industry to discuss new and existing issues concerning evolutionary machine learning and medical science, in particular, the integration between academic research and industry applications, and to stimulate further engagement with the user community. With this Special Issue, we want to disseminate knowledge among researchers, designers, and users in this exciting field.

Topics of interest include, but are not limited to:  
• Artificial Intelligent in Medical Science  
• Medical Image and Video Acquisition  
• Machine learning and Biomedical Image Processing  
• Artificial Intelligence and Health Information Technology  
• Biophysics, Medical Physics, Medical Imaging Technologies  
• Medical Imaging Learning for 3D Understanding and Reconstruction  
• Graph Mining  
• Graph Pattern Matching  
• Graph Neural Networks for Visual Applications  
• Explainable Theory for Graph Neural Networks  
• Graph Neural Network-based Designs for Dynamic Complex Systems  
• Graph Diffusion Network for High Quality Node Representation Learning  
• Graph Feature Fusion and Presentation  
• Semi-supervised/ Unsupervised Graph Clustering  
• Active Learning/ Reinforce Learning for Graph Data

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