LEADER 04830nam 2201165z- 450 001 9910557353503321 005 20231214133404.0 035 $a(CKB)5400000000042355 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/77043 035 $a(EXLCZ)995400000000042355 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Computational Methods for Oncological Image Analysis 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (262 p.) 311 $a3-0365-2554-8 311 $a3-0365-2555-6 330 $a[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians? unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations?such as segmentation, co-registration, classification, and dimensionality reduction?and multi-omics data integration.] 606 $aMedicine$2bicssc 610 $amelanoma detection 610 $adeep learning 610 $atransfer learning 610 $aensemble classification 610 $a3D-CNN 610 $aimmunotherapy 610 $aradiomics 610 $aself-attention 610 $abreast imaging 610 $amicrowave imaging 610 $aimage reconstruction 610 $asegmentation 610 $aunsupervised machine learning 610 $ak-means clustering 610 $aKolmogorov-Smirnov hypothesis test 610 $astatistical inference 610 $aperformance metrics 610 $acontrast source inversion 610 $abrain tumor segmentation 610 $amagnetic resonance imaging 610 $asurvey 610 $abrain MRI image 610 $atumor region 610 $askull stripping 610 $aregion growing 610 $aU-Net 610 $aBRATS dataset 610 $aincoherent imaging 610 $aclutter rejection 610 $abreast cancer detection 610 $aMRgFUS 610 $aproton resonance frequency shift 610 $atemperature variations 610 $areferenceless thermometry 610 $aRBF neural networks 610 $ainterferometric optical fibers 610 $abreast cancer 610 $arisk assessment 610 $amachine learning 610 $atexture 610 $amammography 610 $amedical imaging 610 $aimaging biomarkers 610 $abone scintigraphy 610 $aprostate cancer 610 $asemisupervised classification 610 $afalse positives reduction 610 $acomputer-aided detection 610 $abreast mass 610 $amass detection 610 $amass segmentation 610 $aMask R-CNN 610 $adataset partition 610 $abrain tumor 610 $aclassification 610 $ashallow machine learning 610 $abreast cancer diagnosis 610 $aWisconsin Breast Cancer Dataset 610 $afeature selection 610 $adimensionality reduction 610 $aprincipal component analysis 610 $aensemble method 615 7$aMedicine 700 $aRundo$b Leonardo$4edt$01290017 702 $aMilitello$b Carmelo$4edt 702 $aConti$b Vincenzo$4edt 702 $aZaccagna$b Fulvio$4edt 702 $aHan$b Changhee$4edt 702 $aRundo$b Leonardo$4oth 702 $aMilitello$b Carmelo$4oth 702 $aConti$b Vincenzo$4oth 702 $aZaccagna$b Fulvio$4oth 702 $aHan$b Changhee$4oth 906 $aBOOK 912 $a9910557353503321 996 $aAdvanced Computational Methods for Oncological Image Analysis$93021302 997 $aUNINA