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Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue.

Technology: general issues --- pancreas --- segmentation --- computed tomography --- deep learning --- data augmentation --- neoplasm metastasis --- ovarian neoplasms --- radiation exposure --- tomography --- x-ray computed --- prostate carcinoma --- microscopic --- convolutional neural network --- machine learning --- handcrafted --- oral carcinoma --- medical image segmentation --- colon cancer --- colon polyps --- OCT --- optical biopsy --- animal rat models --- CADx --- airway volume analysis --- artificial intelligence --- coronary artery disease --- SPECT MPI scans --- convolutional neural networks --- transfer learning --- classification models

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Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for the qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modelling such as machine learning have developed rapidly. All of these types of data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more. In this Special Issue, we have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level.

Technology: general issues --- History of engineering & technology --- water temperature --- bathing --- ECG --- heart rate variability --- quantitative analysis --- t-test --- hypertrophic cardiomyopathy --- data mining --- automated curation --- molecular mechanisms --- atrial fibrillation --- sudden cardiac death --- heart failure --- left ventricular outflow tract obstruction --- cardiac fibrosis --- myocardial ischemia --- compound–protein interaction --- Jamu --- machine learning --- drug discovery --- herbal medicine --- data augmentation --- deep learning --- ECG quality assessment --- drug–target interactions --- protein–protein interactions --- chronic diseases --- drug repurposing --- maximum flow --- adenosine methylation --- m6A --- RNA modification --- neuronal development --- genetic variation --- copy number variants --- disease-related traits --- sequential order --- association test --- blood pressure --- cuffless measurement --- longitudinal experiment --- plethysmograph --- nonlinear regression --- n/a --- compound-protein interaction --- drug-target interactions --- protein-protein interactions

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Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for the qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modelling such as machine learning have developed rapidly. All of these types of data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more. In this Special Issue, we have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level.

Technology: general issues --- History of engineering & technology --- water temperature --- bathing --- ECG --- heart rate variability --- quantitative analysis --- t-test --- hypertrophic cardiomyopathy --- data mining --- automated curation --- molecular mechanisms --- atrial fibrillation --- sudden cardiac death --- heart failure --- left ventricular outflow tract obstruction --- cardiac fibrosis --- myocardial ischemia --- compound-protein interaction --- Jamu --- machine learning --- drug discovery --- herbal medicine --- data augmentation --- deep learning --- ECG quality assessment --- drug-target interactions --- protein-protein interactions --- chronic diseases --- drug repurposing --- maximum flow --- adenosine methylation --- m6A --- RNA modification --- neuronal development --- genetic variation --- copy number variants --- disease-related traits --- sequential order --- association test --- blood pressure --- cuffless measurement --- longitudinal experiment --- plethysmograph --- nonlinear regression

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Recent advances in electronics have led to sensors whose sizes and weights are such that they can be placed on living systems without impairing their natural motion and habits. They may be worn on the body as accessories or as part of the clothing and enable personalized mobile information processing. Wearable sensors open the way for a nonintrusive and continuous monitoring of body orientation, movements, and various physiological parameters during motor activities in real-life settings. Thus, they may become crucial tools not only for researchers, but also for clinicians, as they have the potential to improve diagnosis, better monitor disease development and thereby individualize treatment. Wearable sensors should obviously go unnoticed for the people wearing them and be intuitive in their installation. They should come with wireless connectivity and low-power consumption. Moreover, the electronics system should be self-calibrating and deliver correct information that is easy to interpret. Cross-platform interfaces that provide secure data storage and easy data analysis and visualization are needed.This book contains a selection of research papers presenting new results addressing the above challenges.

Medical equipment & techniques --- inertial measurement unit --- movement analysis --- long-track speed skating --- validity --- IMU --- principal component analysis --- wearable --- scoring --- carving --- balance assessment --- data augmentation --- gated recurrent unit --- human activity recognition --- one-dimensional convolutional neural network --- intermittent claudication --- vascular rehabilitation --- 6 min walking test --- functional walking --- TUG --- kinematics --- fall risk --- logistic regression --- elderly --- inertial sensor --- artificial intelligence --- supervised machine learning --- head rotation test --- neck pain --- cerebral palsy --- dystonia --- choreoathetosis --- machine learning --- home-based --- wearable device --- MLP --- gesture recognition --- flex sensor --- model search --- neural network --- inertial measurement unit—IMU --- movement complexity --- sample entropy --- trunk flexion --- low back pain --- lifting technique --- camera system --- ward clustering method --- K-means clustering method --- ensemble clustering method --- Bayesian neural network --- pain self-efficacy questionnaire --- n/a --- inertial measurement unit-IMU

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Artificial intelligence technologies are also actively applied to broadcasting and multimedia processing technologies. A lot of research has been conducted in a wide variety of fields, such as content creation, transmission, and security, and these attempts have been made in the past two to three years to improve image, video, speech, and other data compression efficiency in areas related to MPEG media processing technology. Additionally, technologies such as media creation, processing, editing, and creating scenarios are very important areas of research in multimedia processing and engineering. This book contains a collection of some topics broadly across advanced computational intelligence algorithms and technologies for emerging multimedia signal processing as: Computer vision field, speech/sound/text processing, and content analysis/information mining.

Technology: general issues --- History of engineering & technology --- human-height estimation --- depth video --- depth 3D conversion --- artificial intelligence --- convolutional neural networks --- deep neural network --- convolutional neural network --- environmental sound recognition --- feature combination --- multimodal joint representation --- content curation social networks --- different recommend tasks --- content based recommend systems --- scene/place classification --- semantic segmentation --- deep learning --- weighting matrix --- speech enhancement --- generative adversarial network --- relativistic GAN --- lightweight neural network --- single image super-resolution --- image enhancement --- image restoration --- residual dense networks --- visual sentiment analysis --- sentiment classification --- graph convolutional networks --- generative adversarial networks --- traffic surveillance image processing --- image de-raining --- fluency evaluation --- speech recognition --- data augmentation --- variational autoencoder --- speech conversion --- heartbeat classification --- convolutional neural network (CNN) --- canonical correlation analysis (CCA) --- Indian Sign Language (ISL) --- natural language processing --- avatar --- sign movement --- context-free grammar --- object detection --- logical story unit detection (LSU) --- object re-ID --- computer vision --- image processing --- single image artifacts reduction --- dense networks --- residual networks --- channel attention networks --- n/a