LEADER 04258nam 22008775 450 001 996465948303316 005 20200701125331.0 010 $a3-642-35428-9 024 7 $a10.1007/978-3-642-35428-1 035 $a(CKB)3400000000102912 035 $a(SSID)ssj0000810208 035 $a(PQKBManifestationID)11446159 035 $a(PQKBTitleCode)TC0000810208 035 $a(PQKBWorkID)10827206 035 $a(PQKB)10850671 035 $a(DE-He213)978-3-642-35428-1 035 $a(MiAaPQ)EBC6284791 035 $a(MiAaPQ)EBC5576936 035 $a(Au-PeEL)EBL5576936 035 $a(OCoLC)820211453 035 $a(PPN)168328658 035 $a(EXLCZ)993400000000102912 100 $a20121116d2012 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning in Medical Imaging$b[electronic resource] $eThird International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012, Revised Selected Papers /$fedited by Fei Wang, Dinggang Shen, Pingkun Yan, Kenji Suzuki 205 $a1st ed. 2012. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2012. 215 $a1 online resource (XII, 276 p. 91 illus.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v7588 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-35427-0 320 $aIncludes bibliographical references and author index. 330 $aThis book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Medical Imaging, MLMI 2012, held in conjunction with MICCAI 2012, in Nice, France, in October 2012. The 33 revised full papers presented were carefully reviewed and selected from 67 submissions. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. 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Tetko 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XXXVIII, 176 p. 50 illus., 49 illus. in color.) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14894 311 08$a9783031723803 311 08$a3031723805 330 $aThis open Access book constitutes the refereed proceedings of the First International Workshop on AI in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. The 12 papers presented here were carefully reviewed and selected for these open access proceedings. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v14894 606 $aArtificial intelligence 606 $aData mining 606 $aChemistry$xData processing 606 $aArtificial Intelligence 606 $aData Mining and Knowledge Discovery 606 $aComputational Chemistry 615 0$aArtificial intelligence. 615 0$aData mining. 615 0$aChemistry$xData processing. 615 14$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aComputational Chemistry. 676 $a006.3 702 $aClevert$b Djork-Arné$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWand$b Michael$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMalinovská$b Kristína$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSchmidhuber$b Ju?rgen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTetko$b Igor V.$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910890900403321 996 $aAI in Drug Discovery$94219699 997 $aUNINA