LEADER 01192nam2-2200385---450- 001 990003370720203316 005 20100211161446.0 010 $a978-888908217-1 035 $a000337072 035 $aUSA01000337072 035 $a(ALEPH)000337072USA01 035 $a000337072 100 $a20100211d2008----km-y0itay50------ba 101 $aita 102 $aIT 105 $aa---||||001yy 200 1 $a<> Piemonte$eda Amedeo Lavy a Medardo Rosso$fAlfonso Panzetta 210 $aTorino$cAdarte$d2008 215 $a252 p.$cill.$d30 cm 225 2 $aSculturadarte 410 0$12001$aSculturadarte 461 1$1001000337071$12001$aAtlante regionale degli scultori italiani dal Neoclassicismo al primo Novecento 606 0 $aScultori$yPiemonte$zSec. 18.- 20.$2BNCF 676 $a730.9451 700 1$aPANZETTA,$bAlfonso$0267580 801 0$aIT$bsalbc$gISBD 912 $a990003370720203316 951 $aXII.2.C. 1861/1$b1167 L.G.$cXII.2.$d00015571 959 $aBK 969 $aUMA 979 $aGIUSY$b90$c20100211$lUSA01$h1609 979 $aGIUSY$b90$c20100211$lUSA01$h1612 979 $aGIUSY$b90$c20100211$lUSA01$h1614 996 $aPiemonte$91123416 997 $aUNISA LEADER 01252nam 2200349Ia 450 001 996396520303316 005 20221108025007.0 035 $a(CKB)4330000000351265 035 $a(EEBO)2240952589 035 $a(OCoLC)12075877 035 $a(EXLCZ)994330000000351265 100 $a19850524d1641 uy | 101 0 $alat 135 $aurbn||||a|bb| 200 10$aTerence in English$b[electronic resource] $eFabulae comici facetissimi et elegantissimi poetę Terentii omnes anglicę factę & hac nova forma editę /$fopera ac industria R.B. .. 205 $aSexta editio multo? emendatior ... 210 $aLondon $cPrinted by Iohn Legatt, and are to be sold by Andrew Crooke ...$d1641 215 $a[9], 3-428 p 300 $aLatin text in single column, with marginal notes; English translation in double columns. 300 $aDedication signed by the translator: Rich. Bernard. 300 $aReproduction of original in Huntington Library. 330 $aeebo-0113 700 $aTerence$0439355 701 $aBernard$b Richard$f1568-1641.$0793131 801 0$bEAA 801 1$bEAA 801 2$bm/c 801 2$bWaOLN 906 $aBOOK 912 $a996396520303316 996 $aTerence in English$92301161 997 $aUNISA LEADER 09089nam 2200493 450 001 996464421703316 005 20220815215706.0 010 $a3-030-90874-7 035 $a(MiAaPQ)EBC6804030 035 $a(Au-PeEL)EBL6804030 035 $a(CKB)19410535800041 035 $a(OCoLC)1285428910 035 $a(PPN)258838663 035 $a(EXLCZ)9919410535800041 100 $a20220815d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aClinical image-based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and secure and privacy-preserving machine learning $e10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, proceedings /$fCristina Oyarzun Laura [and three others] editors 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (201 pages) 225 1 $aLecture Notes in Computer Science ;$v12969 311 08$aPrint version: Oyarzun Laura, Cristina Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning Cham : Springer International Publishing AG,c2021 9783030908737 327 $aIntro -- Additional Editors -- CLIP Preface -- CLIP Organization -- DCL Preface -- DCL Organization -- LL-COVID-19 Preface -- LL-COVID-19 Organization -- PPML Preface -- PPML Organization -- Contents -- CLIP -- Intestine Segmentation with Small Computational Cost for Diagnosis Assistance of Ileus and Intestinal Obstruction -- 1 Introduction -- 2 Methods -- 2.1 Overview -- 2.2 Distance Map Estimation for Preventing Incorrect Shortcuts -- 2.3 Graph-Based Segmentation and Visualization -- 3 Experimental Results -- 3.1 Experimental Setup -- 3.2 Evaluations -- 4 Discussion -- 5 Conclusions -- References -- Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning -- 1 Introduction -- 2 Methods -- 2.1 Overview -- 2.2 Patient-Specific, Ligamentoskeletal, Finite Element Mesh Generation -- 3 Results -- 3.1 Datasets -- 3.2 Quantitative Results -- 3.3 Qualitative Results -- 4 Conclusion -- References -- Bayesian Graph Neural Networks for EEG-Based Emotion Recognition -- 1 Introduction -- 2 Methods -- 2.1 Bayesian Graph Neural Networks -- 2.2 Sparse Graph Variational Auto-encoder -- 2.3 Algorithm for BGNN -- 3 Experiments -- 3.1 Datasets -- 3.2 Classification Settings -- 3.3 Results -- 4 Discussion -- 4.1 Ablation Study -- 4.2 Latent Communities -- 5 Conclusions -- References -- ViTBIS: Vision Transformer for Biomedical Image Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Convolutional Neural Network -- 2.2 Attention Mechanism -- 2.3 Transformers -- 2.4 Background -- 3 Method -- 3.1 Dataset -- 3.2 Network Architecture -- 3.3 Residual Connection -- 3.4 Loss Function -- 3.5 Evaluation Metrics -- 3.6 Implementation Details -- 4 Results -- 4.1 Ablation Studies -- 5 Conclusions -- References -- Attention-Guided Pancreatic Duct Segmentation from Abdominal CT Volumes -- 1 Introduction -- 2 Methods. 327 $a2.1 Pancreatic Attention-Guide -- 2.2 Multi-scale Aggregation -- 3 Experiments and Results -- 3.1 Dataset and Settings -- 3.2 Segmentation Results and Discussion -- 4 Conclusion -- References -- Development of the Next Generation Hand-Held Doppler with Waveform Phasicity Predictive Capabilities Using Deep Learning -- 1 Introduction -- 1.1 Background -- 1.2 Innovation -- 1.3 Implementation Summary -- 2 Methods -- 2.1 Data Preparation -- 2.2 Model Development -- 2.3 Hardware Platform -- 3 Results -- 3.1 Baseline Validation -- 3.2 Manual Experiment -- 4 Conclusion -- References -- Learning from Mistakes: An Error-Driven Mechanism to Improve Segmentation Performance Based on Expert Feedback -- 1 Introduction -- 2 Data -- 3 Method -- 4 Experiments and Results -- 4.1 Proof of Concept: Recovering Systematic Errors -- 4.2 Clinical Application: Predicting Expert Corrections -- 5 Discussion and Conclusion -- References -- TMJOAI: An Artificial Web-Based Intelligence Tool for Early Diagnosis of the Temporomandibular Joint Osteoarthritis -- 1 Introduction -- 2 Dataset -- 3 Proposed Methods -- 3.1 Feature Selection -- 3.2 Comparison of Multiple Machine Learning Algorithms -- 3.3 Histogram Matching -- 4 Experimental Results -- 4.1 Experiments -- 4.2 Algorithm Comparison Results -- 4.3 Histogram Matching and Mandibular Fossa Features Results -- 4.4 Deployment -- 5 Conclusion -- References -- COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty -- 1 Introduction -- 2 Method -- 2.1 Infection Region Segmentation by ISNet -- 2.2 Scale Uncertainty-Aware Prediction Aggregation -- 3 Experiments and Results -- 3.1 Ablation and Comparative Study of ISNet -- 3.2 Segmentation by Aggregation FCN -- 4 Discussion and Conclusions -- References -- DCL -- Multi-task Federated Learning for Heterogeneous Pancreas Segmentation -- 1 Introduction -- 2 Methods. 327 $a2.1 FedAvg -- 2.2 FedProx -- 2.3 Dynamic Task Prioritization -- 2.4 Dynamic Weight Averaging -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Experimental Details -- 3.3 Results -- 4 Discussion -- 5 Conclusion -- References -- Federated Learning in the Cloud for Analysis of Medical Images - Experience with Open Source Frameworks -- 1 Introduction -- 2 Related Work -- 3 Dataset Used in Evaluation -- 4 Overview of Available Open Source Frameworks for FL -- 4.1 TensorFlow Federated -- 4.2 PySyft -- 4.3 Flower -- 5 Experiment Setup -- 6 Results -- 6.1 Results for EfficientNetB0 Architecture -- 6.2 Results for ResNet50 Architecture -- 7 Conclusion -- References -- On the Fairness of Swarm Learning in Skin Lesion Classification -- 1 Introduction -- 2 Related Works -- 2.1 Collaborative Learning and Their Application on Healthcare -- 2.2 Security and Privacy of Federated Learning -- 2.3 Fairness -- 3 Problem Setting and Methods -- 3.1 Problem Setting -- 3.2 Swarm Learning -- 3.3 Local and Centralized Training -- 3.4 Fairness Definition and Metrics -- 4 Experiment and Results -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Biases in Models Trained with Different Strategies -- 5 Discussion and Conclusion -- References -- LL-COVID19 -- Lessons Learned from the Development and Application of Medical Imaging-Based AI Technologies for Combating COVID-19: Why Discuss, What Next -- 1 Introduction -- 2 Data Definition -- 3 Data Availability -- 4 Translational Research -- 5 Summary and Next Steps -- References -- The Role of Pleura and Adipose in Lung Ultrasound AI -- 1 Introduction -- 2 Methodology -- 2.1 SubQ Masking -- 2.2 Data -- 2.3 Architecture -- 2.4 Training Strategy -- 3 Experiments -- 4 Results and Discussions -- 5 Conclusion -- References -- DuCN: Dual-Children Network for Medical Diagnosis and Similar Case Recommendation Towards COVID-19. 327 $a1 Introduction -- 2 Method -- 2.1 Proposed Model -- 2.2 Dual-Children Network -- 2.3 Loss Functions -- 3 Experiments and Results -- 3.1 Dataset and Experiments -- 3.2 Results -- 3.3 Ablation Study -- 4 Discussion and Conclusions -- References -- PPML -- Data Imputation and Reconstruction of Distributed Parkinson's Disease Clinical Assessments: A Comparative Evaluation of Two Aggregation Algorithms -- 1 Introduction -- 1.1 Clinical Assessments and Challenges -- 1.2 Contributions -- 2 Related Work -- 3 Methods -- 3.1 Data -- 3.2 Model Setup -- 3.3 Aggregation Algorithms -- 4 Experimental Results -- 4.1 Effect of Number of Missing Modalities During Training -- 4.2 Effect of Number of Missing Values During Evaluation -- 5 Discussion and Conclusion -- References -- Defending Medical Image Diagnostics Against Privacy Attacks Using Generative Methods: Application to Retinal Diagnostics -- 1 Introduction -- 2 Background -- 3 Prior Work -- 4 Methodology -- 4.1 Threat Model -- 4.2 Approach for Data Producer to Defend Privacy -- 4.3 Novel Metric Balancing Utility and Privacy -- 5 Experiments -- 5.1 Dataset -- 5.2 Results -- 6 Discussion and Limitations -- 7 Conclusion -- References -- Author Index. 410 0$aLecture notes in computer science ;$v12969. 606 $aDiagnostic imaging$xData processing$vCongresses 606 $aArtificial intelligence$xMedical applications$vCongresses 615 0$aDiagnostic imaging$xData processing 615 0$aArtificial intelligence$xMedical applications 676 $a616.07540285 702 $aLaura$b Cristina Oyarzun 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464421703316 996 $aClinical image-based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and secure and privacy-preserving machine learning$92905994 997 $aUNISA