LEADER 02091nam 2200481 450 001 9910460410703321 005 20200123112333.0 010 $a2-8062-5694-1 035 $a(CKB)3710000000401463 035 $a(EBL)2030196 035 $a(MiAaPQ)EBC2030196 035 $a(PPN)233409157 035 $a(Au-PeEL)EBL2030196 035 $a(OCoLC)894042918 035 $a(EXLCZ)993710000000401463 100 $a20200123d2014 uy 0 101 0 $afre 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aLa gestalt psychologie de la forme $el'environnement et les formes influencent-ils nos de?cisions? /$fpar Nicolas Crombez ; avec collaboration d'Anne-Christine Cadiat 210 1$a[Place of publication not identified] :$c50Minutes,$d[2014] 210 4$d©2014 215 $a1 online resource (44 p.) 225 1 $aGestion & marketing ;$vNume?ro 7 300 $aDescription based upon print version of record. 311 $a2-8062-5695-X 327 $aPage de titre; La théorie de la Gestalt; Données-clés; Introduction; Historique; Définition du modèle; Théorie - Présentation du concept; En psychologie; En marketing; En leadership; Limites du modèle et extensions; Limites et critiques du modèle; En marketing; En leadership; Extensions et modèles connexes; En marketing; En leadership; Mise en pratique du concept; Conseils et best practices; En marketing; En leadership; Étude de cas; En marketing; En leadership; En résumé; Pour aller plus loin; Sources bibliographiques; Copyright 410 0$aGestion & marketing.$lEnglish ;$vNume?ro 7. 606 $aGestalt psychology 608 $aElectronic books. 615 0$aGestalt psychology. 676 $a150.1982 700 $aCrombez$b Nicolas$0877422 702 $aCadiat$b Anne-Christine 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910460410703321 996 $aLa gestalt psychologie de la forme$91959256 997 $aUNINA LEADER 05908nam 22007575 450 001 996418313003316 005 20201203083022.0 010 $a3-030-50402-6 024 7 $a10.1007/978-3-030-50402-1 035 $a(CKB)5310000000016573 035 $a(MiAaPQ)EBC6235537 035 $a(DE-He213)978-3-030-50402-1 035 $a(PPN)248595318 035 $a(EXLCZ)995310000000016573 100 $a20200620d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial Intelligence and Machine Learning for Digital Pathology$b[electronic resource] $eState-of-the-Art and Future Challenges /$fedited by Andreas Holzinger, Randy Goebel, Michael Mengel, Heimo Müller 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (xii, 339 pages) 225 1 $aLecture Notes in Artificial Intelligence ;$v12090 300 $aIncludes index. 311 $a3-030-50401-8 327 $aExpectations of Artificial Intelligence for Pathology -- Interpretable Deep Neural Network to Predict Estrogen Receptor Status from Haematoxylin-Eosin Images -- Supporting the Donation of Health Records to Biobanks for Medical Research -- Survey of XAI in Digital Pathology -- Sample Quality as Basic Prerequisite for Data Quality: A Quality Management System for Biobanks -- Black Box Nature of Deep Learning for Digital Pathology: Beyond Quantitative to Qualitative Algorithmic Performances -- Towards a Better Understanding of the Workflows: Modeling Pathology Processes in View of Future AI Integration -- OBDEX ? Open Block Data Exchange System -- Image Processing and Machine Learning Techniques for Diabetic Retinopathy Detection: A Review -- Higher Education Teaching Material on Machine Learning in the Domain of Digital Pathology -- Classification vs Deep Learning in Cancer Degree on Limited Histopathology Datasets -- Biobanks and Biobank-Based Artificial Intelligence (AI) Implementation Through an International Lens -- HistoMapr: An Explainable AI (xAI) Platform for Computational Pathology Solutions -- Extension of the Identity Management System Mainzelliste to Reduce Runtimes for Patient Registration in Large Datasets -- Digital Image Analysis in Pathology Using DNA Stain: Contributions in Cancer Diagnostics and Development of Prognostic and Theranostic Biomarkers -- Assessment and Comparison of Colour Fidelity of Whole slide imaging scanners -- Deep Learning Methods for Mitosis Detection in Breast Cancer Histopathological Images: a Comprehensive Review -- Developments in AI and Machine Learning for Neuroimaging. 330 $aData driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ??fit-for-purpose?? samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions. 410 0$aLecture Notes in Artificial Intelligence ;$v12090 606 $aArtificial intelligence 606 $aComputers 606 $aDatabase management 606 $aApplication software 606 $aComputer security 606 $aOptical data processing 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputing Milieux$3https://scigraph.springernature.com/ontologies/product-market-codes/I24008 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aComputer Applications$3https://scigraph.springernature.com/ontologies/product-market-codes/I23001 606 $aSystems and Data Security$3https://scigraph.springernature.com/ontologies/product-market-codes/I28060 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 615 0$aArtificial intelligence. 615 0$aComputers. 615 0$aDatabase management. 615 0$aApplication software. 615 0$aComputer security. 615 0$aOptical data processing. 615 14$aArtificial Intelligence. 615 24$aComputing Milieux. 615 24$aDatabase Management. 615 24$aComputer Applications. 615 24$aSystems and Data Security. 615 24$aImage Processing and Computer Vision. 676 $a616.07540285 702 $aHolzinger$b Andreas$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGoebel$b Randy$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMengel$b Michael$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMüller$b Heimo$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418313003316 996 $aArtificial Intelligence and Machine Learning for Digital Pathology$92004362 997 $aUNISA LEADER 01140nam 2200373 450 001 9910818656003321 005 20230817185934.0 010 $a88-921-8369-9 035 $a(CKB)4950000000159838 035 $a(MiAaPQ)EBC5897347 035 $a(EXLCZ)994950000000159838 100 $a20191120d2019 uy 0 101 0 $aita 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aLa competitivita nel concordato preventivo $ele proposte e le offerte concorrenti /$fMarco Aiello 210 1$aTorino :$cG. Giappichelli Editore,$d2019. 215 $a1 online resource (321 pages) 225 1 $aDiritto commerciale interno e internazionale ;$v71 311 $a88-921-2072-7 410 0$aDiritto commerciale interno e internazionale ;$v71. 606 $aCompetition 615 0$aCompetition. 676 $a338.6048 700 $aAiello$b Marco$0768486 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910818656003321 996 $aLa competitivita nel concordato preventivo$94095276 997 $aUNINA