LEADER 03780nam 2200925z- 450 001 9910566475403321 005 20231214133441.0 035 $a(CKB)5680000000037625 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/81101 035 $a(EXLCZ)995680000000037625 100 $a20202205d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Biomedical Application 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (198 p.) 311 $a3-0365-3445-8 311 $a3-0365-3446-6 330 $aBiomedicine 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. 606 $aResearch & information: general$2bicssc 610 $adepthwise separable convolution (DSC) 610 $aall convolutional network (ACN) 610 $abatch normalization (BN) 610 $aensemble convolutional neural network (ECNN) 610 $aelectrocardiogram (ECG) 610 $aMIT-BIH database 610 $acephalometric landmark 610 $aX-ray 610 $adeep learning 610 $aResNet 610 $aregistration 610 $aelectronic human-machine interface 610 $ablindness 610 $agesture recognition 610 $ainertial sensors 610 $aIMU 610 $adynamic contrast-enhanced MRI 610 $akidney perfusion 610 $aglomerular filtration rate 610 $apharmacokinetic modeling 610 $amulti-layer perceptron 610 $aparameter estimation 610 $ainstance segmentation 610 $acomputer vision 610 $aretinal blood vessel image 610 $acomputer-aided diagnosis 610 $aU-shaped neural network 610 $aresidual learning 610 $asemantic gap 610 $aintracranial hemorrhage 610 $acomputed tomography 610 $arandom forest 610 $asleep disorder 610 $aobstructive sleep disorder 610 $aovernight polysomnogram 610 $aEEG 610 $aEMG 610 $aECG 610 $aHRV signals 610 $aElectronic Medical Record (EMR) 610 $adisease prediction 610 $aAmyotrophic Lateral Sclerosis (ALS) 610 $aweighted Jaccard index (WJI) 610 $alung cancer 610 $aCT images 610 $aCNN 610 $apulmonary fibrosis 610 $aradiotherapy 615 7$aResearch & information: general 700 $aStrzelecki$b Micha?$4edt$01319558 702 $aBadura$b Pawel$4edt 702 $aStrzelecki$b Micha?$4oth 702 $aBadura$b Pawel$4oth 906 $aBOOK 912 $a9910566475403321 996 $aMachine Learning for Biomedical Application$93033964 997 $aUNINA