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Machine Learning for Biomedical Application

Edited by
March 2022
198 pages
  • ISBN978-3-0365-3445-9 (Hardback)
  • ISBN978-3-0365-3446-6 (PDF)
https://doi.org/10.3390/books978-3-0365-3446-6
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This book is a reprint of the Special Issue Machine Learning for Biomedical Application that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
depthwise separable convolution (DSC); all convolutional network (ACN); batch normalization (BN); ensemble convolutional neural network (ECNN); electrocardiogram (ECG); MIT-BIH database; cephalometric landmark; X-ray; deep learning; ResNet; registration; electronic human-machine interface; blindness; gesture recognition; inertial sensors; IMU; dynamic contrast-enhanced MRI; kidney perfusion; glomerular filtration rate; pharmacokinetic modeling; multi-layer perceptron; parameter estimation; instance segmentation; deep learning; computer vision; retinal blood vessel image; computer-aided diagnosis; U-shaped neural network; residual learning; semantic gap; intracranial hemorrhage; computer-aided diagnosis; computed tomography; deep learning; random forest; sleep disorder; obstructive sleep disorder; overnight polysomnogram; EEG; EMG; ECG; HRV signals; deep learning; Electronic Medical Record (EMR); disease prediction; Amyotrophic Lateral Sclerosis (ALS); weighted Jaccard index (WJI); lung cancer; CT images; CNN; pulmonary fibrosis; radiotherapy; n/a