LEADER 00909nam0-22002891i-450- 001 990000463690403321 005 20070618150449.0 035 $a000046369 035 $aFED01000046369 035 $a(Aleph)000046369FED01 035 $a000046369 100 $a20020821d1948----km-y0itay50------ba 101 0 $afre 105 $aa-------001yy 200 1 $aDu rhône au Tennessee$el'equipement hydroélectrique et l'équipement monétaire solidaires de l'équation de Fisher Mc=pT$fJean Labadié 210 $aParis$cHermann$d1948 225 1 $aActualités scientifiques et industrielles$v1062 610 0 $aUtilizzazione dell'energia - Moneta 676 $a333.401 700 1$aLabadié,$bJean$0416628 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000463690403321 952 $a10 D II 48$b2870$fDINEL 959 $aDINEL 996 $aDu rhône au Tennessee$9334898 997 $aUNINA LEADER 02630nam 2200637 a 450 001 9910455863603321 005 20200520144314.0 010 $a1-282-76155-2 010 $a9786612761553 010 $a981-4287-57-1 035 $a(CKB)2490000000001792 035 $a(EBL)731283 035 $a(OCoLC)670429781 035 $a(SSID)ssj0000423962 035 $a(PQKBManifestationID)12121818 035 $a(PQKBTitleCode)TC0000423962 035 $a(PQKBWorkID)10470895 035 $a(PQKB)10553242 035 $a(MiAaPQ)EBC731283 035 $a(WSP)00007467 035 $a(Au-PeEL)EBL731283 035 $a(CaPaEBR)ebr10422057 035 $a(CaONFJC)MIL276155 035 $a(EXLCZ)992490000000001792 100 $a20101103d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 04$aThe new Central Asia$b[electronic resource] $ethe regional impact of international actors /$fedited by Emilian Kavalski 210 $aNew Jersey $cWorld Scientific Pub.$dc2010 215 $a1 online resource (364 p.) 300 $aDescription based upon print version of record. 311 $a981-4287-56-3 320 $aIncludes bibliographical references and index. 327 $apt. 1. The Central Asian agency of international organizations -- pt. 2. The Central Asian agency of states -- pt. 3. Prospects and trajectories for the agency of international actors in Central Asia. 330 $aThis book focuses on Central Asia's place in world affairs and how international politics of state-building has affected the Asian region, thus filling the gaps in ongoing discussions on the rise of Asia in global governance. It also attempts to 'generalize and contextualize the Central Asian experience' and re-evaluate its comparative relevance, by explaining the complex dynamics of Central Asian politics through a detailed analysis of the effects of major international actors - both international organizations as well as current and rising great powers. 606 $aRegionalism$zAsia, Central 606 $aInternational agencies$zAsia, Central 606 $aGeopolitics$zAsia, Central 607 $aAsia, Central$xForeign relations 608 $aElectronic books. 615 0$aRegionalism 615 0$aInternational agencies 615 0$aGeopolitics 676 $a327.58 676 $a340.071/1 701 $aKavalski$b Emilian$0861810 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910455863603321 996 $aThe new Central Asia$92192472 997 $aUNINA LEADER 06726nam 2200469 450 001 996490360903316 005 20230220141430.0 010 $a3-031-16980-8 035 $a(MiAaPQ)EBC7098183 035 $a(Au-PeEL)EBL7098183 035 $a(CKB)24865886200041 035 $a(PPN)264953258 035 $a(EXLCZ)9924865886200041 100 $a20230220d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aSimulation and synthesis in medical imaging $e7th international workshop, SASHIMI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022 : proceedings /$fCan Zhao [and three others] 210 1$aCham, Switzerland :$cSpringer International Publishing,$d[2022] 210 4$d©2022 215 $a1 online resource (176 pages) 225 1 $aLecture Notes in Computer Science Ser. ;$vv.13570 311 08$aPrint version: Zhao, Can Simulation and Synthesis in Medical Imaging Cham : Springer International Publishing AG,c2022 9783031169793 327 $aIntro -- Preface -- Organization -- Contents -- Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images -- 1 Introduction -- 1.1 Related Works -- 1.2 Contributions -- 2 Methods -- 2.1 Generators -- 2.2 Discriminators -- 2.3 Losses -- 3 Experiments -- 3.1 Evaluation -- 3.2 Implementation -- 3.3 Data -- 3.4 Results -- 4 Conclusion -- References -- Generating Artificial Artifacts for Motion Artifact Detection in Chest CT -- 1 Introduction -- 2 Methods -- 3 Experiments -- 4 Results -- 5 Discussion -- References -- Probabilistic Image Diversification to Improve Segmentation in 3D Microscopy Image Data -- 1 Introduction -- 2 Probabilistic Image Diversification -- 3 Experiments and Results -- 3.1 Data Augmentation -- 3.2 Benchmarking -- 3.3 Test-Time Augmentation -- 4 Discussion and Conclusion -- References -- Pathology Synthesis of 3D Consistent Cardiac MR Images Using 2D VAEs and GANs -- 1 Introduction -- 1.1 Contributions -- 2 Method -- 2.1 Pathology Synthesis -- 2.2 Modeling Slice Relationship -- 2.3 Data and Implementation -- 3 Results -- 3.1 Pathology Synthesis -- 3.2 Modeling the Slice Relationship -- 4 Discussion and Conclusion -- References -- .26em plus .1em minus .1emHealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease -- 1 Introduction -- 2 HealthyGAN: The Proposed Method -- 2.1 Network Architecture -- 2.2 Training -- 2.3 Detecting Anomalies -- 3 Experiments and Results -- 3.1 COVID-19 Detection -- 3.2 Chest X-ray 14 Diseases Detection -- 3.3 Migraine Detection -- 4 Conclusion -- A Implementation Details -- B Network Architectures -- B.1 Discriminator -- B.2 Generator -- References -- Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement -- 1 Introduction -- 2 Methodology -- 3 Experiments and Results. 327 $a4 Conclusion -- References -- Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain -- 1 Introduction -- 2 Background -- 2.1 VQ-VAE -- 2.2 Transformer -- 3 Methods -- 3.1 Descriptive Quantization for Transformer Usage -- 3.2 Autoregressive Modelling of the Brain -- 4 Experiments and Results -- 4.1 Quantitative Image Fidelity Evaluation -- 4.2 Morphological Evaluation -- 5 Conclusion -- 6 Appendix -- 6.1 VQ-VAEs -- 6.2 Transformers -- 6.3 Losses -- 6.4 Datasets -- 6.5 VBM Analysis -- References -- Can Segmentation Models Be Trained with Fully Synthetically Generated Data? -- 1 Background -- 2 Materials and Methods -- 2.1 Materials -- 2.2 Methods -- 2.3 Segmentation Network Used for the Experiments -- 3 Experiments -- 3.1 Can We Learn to Segment Healthy Regions Using Synthetic Data? -- 3.2 Can Synthetic Generative Models Address Out-of-Distribution Segmentation? -- 3.3 Can We Learn to Segment Pathologies from Synthetic Data? -- 4 Discussion and Conclusion -- A Training Set-Ups -- A.1 Training brainSPADE -- A.2 Training Segmentation nnU-Nets -- B Additional Figures -- References -- Multimodal Super Resolution with Dual Domain Loss and Gradient Guidance -- 1 Introduction -- 2 Materials and Methods -- 3 Experiments -- 4 Discussion and Conclusion -- References -- Brain Lesion Synthesis via Progressive Adversarial Variational Auto-Encoder -- 1 Introduction -- 2 Methodology -- 2.1 Model Architecture -- 2.2 Condition and Mask Embedding Blocks -- 2.3 Loss Functions -- 3 Experiments -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Evaluation Metrics -- 3.4 Experimental Results -- 4 Discussion and Conclusion -- References -- Contrastive Learning for Generating Optical Coherence Tomography Images of the Retina -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Model Training. 327 $a4.3 Results -- 5 Conclusions -- References -- A Novel Method Combining Global and Local Assessments to Evaluate CBCT-Based Synthetic CTs -- 1 Introduction -- 2 Methods -- 2.1 Data Acquisition and Processing -- 2.2 Validation Methodology -- 3 Results -- 3.1 Validation Methodology -- 4 Discussion -- 5 Conclusion -- References -- SuperFormer: Volumetric Transformer Architectures for MRI Super-Resolution -- 1 Introduction -- 2 Method -- 2.1 Feature Embedding -- 2.2 Volume Embedding -- 2.3 3D Deep Feature Extraction -- 2.4 HQ Volume Reconstruction -- 3 Experimental Setup -- 3.1 Implementation Details -- 3.2 Results -- 4 Conclusion -- References -- Evaluating the Performance of StyleGAN2-ADA on Medical Images -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 Generative Modeling -- 2.3 Evaluation Measures -- 3 Results -- 4 Conclusion -- References -- Backdoor Attack is a Devil in Federated GAN-Based Medical Image Synthesis -- 1 Introduction -- 2 Methods -- 2.1 Federated Generative Adversarial Network -- 2.2 Backdoor Attack Strategies -- 2.3 Defense Strategies -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Implementation of Attack -- 3.3 Implementation of Defense -- 3.4 Results and Discussion -- 4 Conclusion -- A More Experiment Results -- B WGAN-GP with Large Trigger Size -- References -- Author Index. 410 0$aLecture Notes in Computer Science Ser. 606 $aDiagnostic imaging$xData processing 606 $aDiagnostic imaging$xDigital techniques 615 0$aDiagnostic imaging$xData processing. 615 0$aDiagnostic imaging$xDigital techniques. 676 $a616.0754 702 $aZhao$b Can 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996490360903316 996 $aSimulation and Synthesis in Medical Imaging$92916762 997 $aUNISA