LEADER 01403nam2-2200409li-450 001 990000233270203316 005 20180312154823.0 010 $a981-02-1294-1 035 $a0023327 035 $aUSA010023327 035 $a(ALEPH)000023327USA01 035 $a0023327 100 $a2001996021994-------y0itay0103----ba 101 0 $aeng 102 $aGW 200 1 $a<> concise guide to semigroups and evolution equations$fAldo Belleni-Morante 210 $aSingapore [etc.]$cWorld Scientific$dcopyr. 1994 215 $aXII, 164 p.$cill.$d22 cm 225 2 $aSeries on advances in mathematics for applied sciences$v19 410 0$10010023328$12001$aSeries on advances in mathematics for applied sciences 610 1 $aoperatori lineari 610 1 $asemigruppi 676 $a5122$9Algebra astratta (Gruppi) 700 1$aBELLENI-MORANTE,$bAldo$0105195 801 $aSistema bibliotecario di Ateneo dell' Università di Salerno$gRICA 912 $a990000233270203316 951 $a512.2 BEL$b18353/CBS$c512.2$d00219939 959 $aBK 969 $aSCI 979 $c19960202 979 $c20001110$lUSA01$h1714 979 $c20020403$lUSA01$h1631 979 $aPATRY$b90$c20040406$lUSA01$h1617 979 $aRSIAV6$b90$c20090506$lUSA01$h1204 979 $aANDRIA$b90$c20161024$lUSA01$h1645 996 $aConcise guide to semigroups and evolution equations$9911214 997 $aUNISA LEADER 04885nam 2201189z- 450 001 9910557353503321 005 20220111 035 $a(CKB)5400000000042355 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/77043 035 $a(oapen)doab77043 035 $a(EXLCZ)995400000000042355 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvanced Computational Methods for Oncological Image Analysis 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (262 p.) 311 08$a3-0365-2554-8 311 08$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. 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