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Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning
This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. For DART 2020, 12 full papers were accepted from 18 submissions. They deal with methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings by making them robust and consistent across different domains. For DCL 2020...
Information Technology and Intelligent Transportation Systems
Intelligent Transportation Systems (ITS) are the model for integrating advanced information technology, data communication transmission technology, electronic sensing technology, control technology and computer technology into a comprehensive ground traffic management system. They are the direction of development for future transportation systems. This book presents the proceedings of the 3rd International Conference on Information Technology and Intelligent Transportation Systems (ITITS 2018), held in Xi’an, China, on 15-16 September 2018. The conference provides a platform for professionals and researchers from industry and academia to present and discuss recent advances in the field of ...
Computer Vision – ECCV 2020
The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023
The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learnin...
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014
The three-volume set LNCS 8673, 8674, and 8675 constitutes the refereed proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, held in Boston, MA, USA, in September 2014. Based on rigorous peer reviews, the program committee carefully selected 253 revised papers from 862 submissions for presentation in three volumes. The 100 papers included in the second volume have been organized in the following topical sections: biophysical modeling and simulation; atlas-based transfer of boundary conditions for biomechanical simulation; temporal and motion modeling; computer-aided diagnosis; pediatric imaging; endoscopy; ultrasound imaging; machine learning; cardiovascular imaging; intervention planning and guidance; and brain.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in m...
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; op...
Landscape of Next Generation Sequencing Using Pattern Recognition
This book focuses on an eminent technology called next generation sequencing (NGS) which has entirely changed the procedure of examining organisms and will have a great impact on biomedical research and disease diagnosis. Numerous computational challenges have been brought on by the rapid advancement of large-scale next-generation sequencing (NGS) technologies and their application. The term ""biomedical imaging"" refers to the use of a variety of imaging techniques (such as X-rays, CT scans, MRIs, ultrasounds, etc.) to get images of the interior organs of a human being for potential diagnostic, treatment planning, follow-up, and surgical purposes. In these circumstances, deep learning, a ne...
Deep Learning at Scale
Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. You'll gain a thorough understanding of: How...
