LEADER 06115nam 22006375 450 001 9910508452103321 005 20250807130506.0 010 $a3-030-90874-7 024 7 $a10.1007/978-3-030-90874-4 035 $a(MiAaPQ)EBC6804030 035 $a(Au-PeEL)EBL6804030 035 $a(CKB)19410535800041 035 $a(OCoLC)1285428910 035 $a(DE-He213)978-3-030-90874-4 035 $a(PPN)258838663 035 $a(EXLCZ)9919410535800041 100 $a20211113d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aClinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning $e10th Workshop, CLIP 2021, Second Workshop, DCL 2021, First Workshop, LL-COVID19 2021, and First Workshop and Tutorial, PPML 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 and October 1, 2021, Proceedings /$fedited by Cristina Oyarzun Laura, M. Jorge Cardoso, Michal Rosen-Zvi, Georgios Kaissis, Marius George Linguraru, Raj Shekhar, Stefan Wesarg, Marius Erdt, Klaus Drechsler, Yufei Chen, Shadi Albarqouni, Spyridon Bakas, Bennett Landman, Nicola Rieke, Holger Roth, Xiaoxiao Li, Daguang Xu, Maria Gabrani, Ender Konukoglu, Michal Guindy, Daniel Rueckert, Alexander Ziller, Dmitrii Usynin, Jonathan Passerat-Palmbach 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (201 pages) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12969 311 08$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 $aIntestine segmentation with small computational cost for diagnosis assistance of ileus and intestinal obstruction -- Generation of Patient-Specific, Ligamentoskeletal, Finite Element Meshes for Scoliosis Correction Planning -- Bayesian Graph Neural Networks For EEG-based Emotion Recognition -- ViTBIS: Vision Transformer for Biomedical Image Segmentation -- Attention-guided pancreatic duct segmentation from abdominal CT volumes -- Development of the Next Generation Hand-Held Doppler With Waveform Phasicity Predictive Capabilities Using Deep Learning -- Learning from mistakes: an error-driven mechanism to improve segmentation performance based on expert feedback -- TMJOAI: an artificial web-based intelligence tool for early diagnosis of the Temporomandibular Joint Osteoarthritis -- COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty -- Multi-task Federated Learning for Heterogeneous Pancreas Segmentation -- Federated Learning in the Cloud for Analysis of Medical Images- Experience with Open Source Frameworks -- On the Fairness of Swarm Learning in Skin Lesion Classification -- Lessons learned from the development and application of medical imaging-based AI technologies for combating COVID-19: why discuss, what next -- The Role of Pleura and Adipose in Lung Ultrasound AI -- DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19 -- Data imputation and reconstruction of distributed Parkinson's disease clinical assessments: A comparative evaluation of two aggregation algorithms -- Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods: Application to Retinal Diagnostics. 330 $aThis book constitutes the refereed proceedings of the 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, Second MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, First MICCAI Workshop, LL-COVID19, First Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. CLIP 2021 accepted 9 papers from the 13 submissions received. It focuses on holistic patient models for personalized healthcare with the goal to bring basic research methods closer to the clinical practice. For DCL 2021, 4 papers from 7 submissions were accepted for publication. They deal with machine learning applied to problems where data cannot be stored in centralized databases and information privacy is a priority. LL-COVID19 2021 accepted 2 papers out of 3 submissions dealing with the use of AI models in clinical practice. And for PPML 2021, 2 papers were accepted from a total of 6 submissions, exploring the use of privacy techniques in the medical imaging community. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12969 606 $aComputer vision 606 $aMachine learning 606 $aComputer networks 606 $aSocial sciences$xData processing 606 $aComputer Vision 606 $aMachine Learning 606 $aComputer Communication Networks 606 $aComputer Application in Social and Behavioral Sciences 615 0$aComputer vision. 615 0$aMachine learning. 615 0$aComputer networks. 615 0$aSocial sciences$xData processing. 615 14$aComputer Vision. 615 24$aMachine Learning. 615 24$aComputer Communication Networks. 615 24$aComputer Application in Social and Behavioral Sciences. 676 $a616.07540285 702 $aLaura$b Cristina Oyarzun 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910508452103321 996 $aClinical image-based procedures, distributed and collaborative learning, artificial intelligence for combating COVID-19 and secure and privacy-preserving machine learning$92905994 997 $aUNINA