LEADER 03813nam  2200949z- 450 
001 9910566475403321
005 20220506
035   $a(CKB)5680000000037625
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/81101
035   $a(oapen)doab81101
035   $a(EXLCZ)995680000000037625
100   $a20202205d2022      |y         0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 00$aMachine Learning for Biomedical Application
210   $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022
215   $a1 online resource (198 p.)
311 08$a3-0365-3445-8 
311 08$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   $aall convolutional network (ACN)
610   $aAmyotrophic Lateral Sclerosis (ALS)
610   $abatch normalization (BN)
610   $ablindness
610   $acephalometric landmark
610   $aCNN
610   $acomputed tomography
610   $acomputer vision
610   $acomputer-aided diagnosis
610   $aCT images
610   $adeep learning
610   $adepthwise separable convolution (DSC)
610   $adisease prediction
610   $adynamic contrast-enhanced MRI
610   $aECG
610   $aEEG
610   $aelectrocardiogram (ECG)
610   $aelectronic human-machine interface
610   $aElectronic Medical Record (EMR)
610   $aEMG
610   $aensemble convolutional neural network (ECNN)
610   $agesture recognition
610   $aglomerular filtration rate
610   $aHRV signals
610   $aIMU
610   $ainertial sensors
610   $ainstance segmentation
610   $aintracranial hemorrhage
610   $akidney perfusion
610   $alung cancer
610   $aMIT-BIH database
610   $amulti-layer perceptron
610   $an/a
610   $aobstructive sleep disorder
610   $aovernight polysomnogram
610   $aparameter estimation
610   $apharmacokinetic modeling
610   $apulmonary fibrosis
610   $aradiotherapy
610   $arandom forest
610   $aregistration
610   $aresidual learning
610   $aResNet
610   $aretinal blood vessel image
610   $asemantic gap
610   $asleep disorder
610   $aU-shaped neural network
610   $aweighted Jaccard index (WJI)
610   $aX-ray
615  7$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