LEADER 04097nam 22008775 450 001 9910483386103321 005 20230329132349.0 010 $a3-319-12289-4 024 7 $a10.1007/978-3-319-12289-2 035 $a(CKB)3710000000249788 035 $a(SSID)ssj0001354054 035 $a(PQKBManifestationID)11758983 035 $a(PQKBTitleCode)TC0001354054 035 $a(PQKBWorkID)11323194 035 $a(PQKB)11391141 035 $a(DE-He213)978-3-319-12289-2 035 $a(MiAaPQ)EBC6281727 035 $a(MiAaPQ)EBC5587523 035 $a(Au-PeEL)EBL5587523 035 $a(OCoLC)892732549 035 $a(PPN)181352265 035 $a(EXLCZ)993710000000249788 100 $a20140922d2014 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aBayesian and grAphical Models for Biomedical Imaging $eFirst International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers /$fedited by M. Jorge Cardoso, Ivor Simpson, Tal Arbel, Doina Precup, Annemie Ribbens 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (X, 131 p. 54 illus.) 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v8677 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-12288-6 330 $aThis book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2014, held in Cambridge, MA, USA, in September 2014 as a satellite event of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014. The 11 revised full papers presented were carefully reviewed and selected from numerous submissions with a key aspect on probabilistic modeling applied to medical image analysis. The objectives of this workshop compared to other workshops, e.g. machine learning in medical imaging, have a stronger mathematical focus on the foundations of probabilistic modeling and inference. The papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis or for the advancement of modeling and analysis of medical imaging data. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v8677 606 $aAlgorithms 606 $aArtificial intelligence 606 $aComputer vision 606 $aPattern recognition systems 606 $aComputer graphics 606 $aComputer science?Mathematics 606 $aDiscrete mathematics 606 $aAlgorithms 606 $aArtificial Intelligence 606 $aComputer Vision 606 $aAutomated Pattern Recognition 606 $aComputer Graphics 606 $aDiscrete Mathematics in Computer Science 615 0$aAlgorithms. 615 0$aArtificial intelligence. 615 0$aComputer vision. 615 0$aPattern recognition systems. 615 0$aComputer graphics. 615 0$aComputer science?Mathematics. 615 0$aDiscrete mathematics. 615 14$aAlgorithms. 615 24$aArtificial Intelligence. 615 24$aComputer Vision. 615 24$aAutomated Pattern Recognition. 615 24$aComputer Graphics. 615 24$aDiscrete Mathematics in Computer Science. 676 $a005.1 702 $aCardoso$b M. Jorge$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSimpson$b Ivor$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aArbel$b Tal$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPrecup$b Doina$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRibbens$b Annemie$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483386103321 996 $aBayesian and grAphical Models for Biomedical Imaging$92830308 997 $aUNINA