Manuscript Topics

Machine learning (ML) techniques are the studies of computer algorithms that learn automatically through experience. ML is a subset of artificial intelligence. ML algorithms build a mathematical model based on sample data, known as "data-driven models ", in order to make predictions or decisions without being explicitly programmed. On the other side, ML is closely related to optimization. Many ML models are formulated as minimization of some loss function on a training set of examples. The difference between the two fields arises from the goal of generalization: while optimization algorithms attempt to minimize the loss on a training set, ML is concerned with minimizing the loss on unseen samples. ML holds great promise as an addition to the arsenal of analysis and comprehension tools for medical images. Besides, ML can accomplish many tasks such as registration, preprocessing, classification, prediction, inference, etc. This Special Issue plans to report the recent progresses on optimization algorithms and machine learning techniques in Medical Image Analysis. The medical data can be obtained from single/multiple imaging modalities, such as, Computed Tomography, Magnetic Resonance Imaging, Ultrasound, Single Photon Emission Computed Tomography, Positron Emission Tomography, Magnetic Particle Imaging, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy, etc. The ultimate goal is to promote research and development of optimization algorithms and machine learning techniques in medical image analysis by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field. Topics of interest should include, but not limited to λ Supervised/unsupervised learning on registration, preprocessing, classification, prediction over medical images λ Deep learning for medical image/video analysis λ Machine learning and optimization of big data in imaging λ Medical image segmentation, registration, and fusion λ Validation of ML results on medical image analysis λ Explainable ML on medical image analysis λ Computer-aided diagnosis λ Image formation/reconstruction and image quality assessment λ Visualization in medical imaging

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