TY - BOOK ID - 145401225 TI - Machine Learning for Biomedical Application AU - Strzelecki, Michał AU - Badura, Pawel PY - 2022 PB - Basel MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Research & information: general KW - depthwise separable convolution (DSC) KW - all convolutional network (ACN) KW - batch normalization (BN) KW - ensemble convolutional neural network (ECNN) KW - electrocardiogram (ECG) KW - MIT-BIH database KW - cephalometric landmark KW - X-ray KW - deep learning KW - ResNet KW - registration KW - electronic human-machine interface KW - blindness KW - gesture recognition KW - inertial sensors KW - IMU KW - dynamic contrast-enhanced MRI KW - kidney perfusion KW - glomerular filtration rate KW - pharmacokinetic modeling KW - multi-layer perceptron KW - parameter estimation KW - instance segmentation KW - computer vision KW - retinal blood vessel image KW - computer-aided diagnosis KW - U-shaped neural network KW - residual learning KW - semantic gap KW - intracranial hemorrhage KW - computed tomography KW - random forest KW - sleep disorder KW - obstructive sleep disorder KW - overnight polysomnogram KW - EEG KW - EMG KW - ECG KW - HRV signals KW - Electronic Medical Record (EMR) KW - disease prediction KW - Amyotrophic Lateral Sclerosis (ALS) KW - weighted Jaccard index (WJI) KW - lung cancer KW - CT images KW - CNN KW - pulmonary fibrosis KW - radiotherapy KW - depthwise separable convolution (DSC) KW - all convolutional network (ACN) KW - batch normalization (BN) KW - ensemble convolutional neural network (ECNN) KW - electrocardiogram (ECG) KW - MIT-BIH database KW - cephalometric landmark KW - X-ray KW - deep learning KW - ResNet KW - registration KW - electronic human-machine interface KW - blindness KW - gesture recognition KW - inertial sensors KW - IMU KW - dynamic contrast-enhanced MRI KW - kidney perfusion KW - glomerular filtration rate KW - pharmacokinetic modeling KW - multi-layer perceptron KW - parameter estimation KW - instance segmentation KW - computer vision KW - retinal blood vessel image KW - computer-aided diagnosis KW - U-shaped neural network KW - residual learning KW - semantic gap KW - intracranial hemorrhage KW - computed tomography KW - random forest KW - sleep disorder KW - obstructive sleep disorder KW - overnight polysomnogram KW - EEG KW - EMG KW - ECG KW - HRV signals KW - Electronic Medical Record (EMR) KW - disease prediction KW - Amyotrophic Lateral Sclerosis (ALS) KW - weighted Jaccard index (WJI) KW - lung cancer KW - CT images KW - CNN KW - pulmonary fibrosis KW - radiotherapy UR - https://www.unicat.be/uniCat?func=search&query=sysid:145401225 AB - 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. ER -