Machine Learning for Biomedical Application
Strzelecki, Michał
Machine Learning for Biomedical Application - Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 - 1 electronic resource (198 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-3446-6 9783036534459 9783036534466
10.3390/books978-3-0365-3446-6 doi
Research & information: general
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 computer vision retinal blood vessel image computer-aided diagnosis U-shaped neural network residual learning semantic gap intracranial hemorrhage computed tomography random forest sleep disorder obstructive sleep disorder overnight polysomnogram EEG EMG ECG HRV signals Electronic Medical Record (EMR) disease prediction Amyotrophic Lateral Sclerosis (ALS) weighted Jaccard index (WJI) lung cancer CT images CNN pulmonary fibrosis radiotherapy n/a
Machine Learning for Biomedical Application - Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 - 1 electronic resource (198 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-3446-6 9783036534459 9783036534466
10.3390/books978-3-0365-3446-6 doi
Research & information: general
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 computer vision retinal blood vessel image computer-aided diagnosis U-shaped neural network residual learning semantic gap intracranial hemorrhage computed tomography random forest sleep disorder obstructive sleep disorder overnight polysomnogram EEG EMG ECG HRV signals Electronic Medical Record (EMR) disease prediction Amyotrophic Lateral Sclerosis (ALS) weighted Jaccard index (WJI) lung cancer CT images CNN pulmonary fibrosis radiotherapy n/a