00811nam0-2200301---450-99001008743040332120160706143007.03-446-15611-9001008743FED01001008743(Aleph)001008743FED0100100874320160706d1989----km-y0itay50------bagerDE--------001yyLiebesverratdie Treulosen in der LiteraturPeter von MattMünchenHanser1989440 p.23 cmAmore nella letteratura809.93322itaMatt,Peter von<1937- >ITUNINARICAUNIMARCBK990010087430403321809.933 MAT 1Dip.f.m.5376FLFBCFLFBCUNINA03079 am 2200673 n 450 9910214927403321201706212-8218-8567-910.4000/books.pup.7228(CKB)3710000001633432(FrMaCLE)OB-pup-7228(oapen)https://directory.doabooks.org/handle/20.500.12854/54307(PPN)203889886(EXLCZ)99371000000163343220170807j|||||||| ||| 0freuu||||||m||||txtrdacontentcrdamediacrrdacarrierLes narrations de la mort /Régis Bertrand, Anne Carol, Jean-Noël PelenAix-en-Provence Presses universitaires de Provence20171 online resource (298 p.) 2-85399-613-1 Dans le contexte du « retour des morts » observable dans les sciences humaine en ce début de troisième millénaire, cet ouvrage, issu d'un colloque tenu à Aix-en-Provence en 2003, croise les disciplines autour de plusieurs formes caractérisées de narrations de la mort et restitue une épaisseur historique à un phénomène souvent réduit à ses évolutions récentes. Les auteurs se sont attachés à offrir à la comparaison des tentatives discursives d'origine très diverses, issues de niveaux de culture et d'horizons différents, qui visent à donner sens à la mort ou à l'instrumentaliser dans un projet normatif ou édifiant ; ils explorent aussi certaines des multiples formes de mises en scène et de mises en images d'une mort qui s'avère rester intimement mêlée au quotidien des vivants.Death in literatureCongressesLiterature of the deathCritical studiesDeath in literatureAraùjo Ana Cristina716022Attard-Maraninchi Marie-Françoise1309969Barras Vincent1233209Bernos Marcel1238345Bertherat Bruno1292148Bertrand Régis663463Bouvier Jean-Claude76835Carol Anne1284996Coulet Noël252070Cousin Bernard154222Croix Alain212572Guilhaumou Jacques310272la Genardière Claude de1317236Lapied Martine299415Laurence Pierre1317237Lebrun François384987Mazel Claire1317238Nonnis Vigilante Serenella1317239Pelen Jean-Noël1305385Renaudet Isabelle1292153Sborgi Franco303614Villain-Gandossi Christiane484806Vovelle Michel139780Bertrand Régis663463Carol Anne1284996Pelen Jean-Noël1305385FR-FrMaCLEBOOK9910214927403321Les narrations de la mort3032914UNINA01881nam 2200445 450 991081629580332120230807194352.01-4677-8771-X(CKB)4100000005115692(MiAaPQ)EBC5443389(Au-PeEL)EBL5443389(CaPaEBR)ebr11590298(OCoLC)903172813(EXLCZ)99410000000511569220220516d2015 uy pengurcnu||||||||txtrdacontentcrdamediacrrdacarrierDante's inferno the vision of Hell from the divine comedy /by Dante Alighieri ; illustrations by Gustave Doré ; translated by the Rev. Henry Francis, Cary, M.AMinneapolis, Minnesota :First Avenue Editions,[2015]©20151 online resource (232 pages)First Avenue classicsCover -- Title Page -- Copyright Information -- Table of Contents -- CANTO I -- CANTO II -- CANTO III -- CANTO IV -- CANTO V -- CANTO VI -- CANTO VII -- CANTO VIII -- CANTO IX -- CANTO X -- CANTO XI -- CANTO XII -- CANTO XIII -- CANTO XIV -- CANTO XV -- CANTO XVI -- CANTO XVII -- CANTO XVIII -- CANTO XIX -- CANTO XX -- CANTO XXI -- CANTO XXII -- CANTO XXIII -- CANTO XXIV -- CANTO XXV -- CANTO XXVI -- CANTO XXVII -- CANTO XXVIII -- CANTO XXIX -- CANTO XXX -- CANTO XXXI -- CANTO XXXII -- CANTO XXXIII -- CANTO XXXIV -- Back Cover.First Avenue classics.HellPoetryHell851.1Dante Alighieri1265-1321,38904Doré Gustave1832-1883,Cary Henry Francis1772-1844,MiAaPQMiAaPQMiAaPQBOOK9910816295803321Dante's Inferno874315UNINA04885nam 2201189z- 450 991055735350332120220111(CKB)5400000000042355(oapen)https://directory.doabooks.org/handle/20.500.12854/77043(oapen)doab77043(EXLCZ)99540000000004235520202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvanced Computational Methods for Oncological Image AnalysisBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (262 p.)3-0365-2554-8 3-0365-2555-6 [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.]Medicine and Nursingbicssc3D-CNNbone scintigraphybrain MRI imagebrain tumorbrain tumor segmentationBRATS datasetbreast cancerbreast cancer detectionbreast cancer diagnosisbreast imagingbreast massclassificationclutter rejectioncomputer-aided detectioncontrast source inversiondataset partitiondeep learningdimensionality reductionensemble classificationensemble methodfalse positives reductionfeature selectionimage reconstructionimaging biomarkersimmunotherapyincoherent imaginginterferometric optical fibersk-means clusteringKolmogorov-Smirnov hypothesis testmachine learningmagnetic resonance imagingmammographyMask R-CNNmass detectionmass segmentationmedical imagingmelanoma detectionmicrowave imagingMRgFUSn/aperformance metricsprincipal component analysisprostate cancerproton resonance frequency shiftradiomicsRBF neural networksreferenceless thermometryregion growingrisk assessmentsegmentationself-attentionsemisupervised classificationshallow machine learningskull strippingstatistical inferencesurveytemperature variationstexturetransfer learningtumor regionU-Netunsupervised machine learningWisconsin Breast Cancer DatasetMedicine and NursingRundo Leonardoedt1290017Militello CarmeloedtConti VincenzoedtZaccagna FulvioedtHan ChangheeedtRundo LeonardoothMilitello CarmeloothConti VincenzoothZaccagna FulvioothHan ChangheeothBOOK9910557353503321Advanced Computational Methods for Oncological Image Analysis3021302UNINA