02407nam 2200673 a 450 991045635880332120200520144314.01-59332-048-5(CKB)111087028316838(OCoLC)54059311(CaPaEBR)ebrary10056590(SSID)ssj0000114454(PQKBManifestationID)11129085(PQKBTitleCode)TC0000114454(PQKBWorkID)10124421(PQKB)10655902(MiAaPQ)EBC3016716(Au-PeEL)EBL3016716(CaPaEBR)ebr10056590(EXLCZ)9911108702831683820030418d2003 uy 0engurcn|||||||||txtccrBrazilian immigrants in the United States[electronic resource] cultural imperialism and social class /Bernadete Beserra ; preface by Michael KearneyNew York LFB Scholarly Pub.20031 online resource (256 p.) The new AmericansBibliographic Level Mode of Issuance: Monograph1-931202-68-0 Includes bibliographical references (p. 223-237) and index.New Americans (LFB Scholarly Publishing LLC)Brazilian AmericansCaliforniaLos AngelesSocial conditionsBrazilian AmericansCultural assimilationCaliforniaLos AngelesImmigrantsCaliforniaLos AngelesSocial conditionsAmericanizationImperialismSocial aspectsUnited StatesLos Angeles (Calif.)Ethnic relationsBrazilEmigration and immigrationUnited StatesEmigration and immigrationBrazilRelationsUnited StatesUnited StatesRelationsBrazilElectronic books.Brazilian AmericansSocial conditions.Brazilian AmericansCultural assimilationImmigrantsSocial conditions.Americanization.ImperialismSocial aspects979.4/94004698Beserra Bernadete1038449MiAaPQMiAaPQMiAaPQBOOK9910456358803321Brazilian immigrants in the United States2460035UNINA09089nam 2200493 450 99646442170331620220815215706.03-030-90874-7(MiAaPQ)EBC6804030(Au-PeEL)EBL6804030(CKB)19410535800041(OCoLC)1285428910(PPN)258838663(EXLCZ)991941053580004120220815d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierClinical image-based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and secure and privacy-preserving machine learning 10th 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 /Cristina Oyarzun Laura [and three others] editorsCham, Switzerland :Springer,[2021]©20211 online resource (201 pages)Lecture Notes in Computer Science ;12969Print 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 Intro -- 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.2.1 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.2.1 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-19.1 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.Lecture notes in computer science ;12969.Diagnostic imagingData processingCongressesArtificial intelligenceMedical applicationsCongressesDiagnostic imagingData processingArtificial intelligenceMedical applications616.07540285Laura Cristina OyarzunMiAaPQMiAaPQMiAaPQBOOK996464421703316Clinical image-based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and secure and privacy-preserving machine learning2905994UNISA