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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 2020
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applica...
Concepts and Principles of Pharmacology
Celebrating 100 years of HEP, this volume will discuss key pharmacological discoveries and concepts of the past 100 years. These discoveries have dramatically changed the medical treatment paradigms of many diseases and these concepts have and will continue to shape discovery of new medicinies. Newly evolving technologies will similarly be discussed as they will shape the future of the pharmacology and, accordingly, medical therapy.
Artificial Intelligence and Machine Learning for Digital Pathology
Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ‘‘fit-for-purpose’’ samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
The three-volume set LNCS 10433, 10434, and 10435 constitutes the refereed proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, held inQuebec City, Canada, in September 2017. The 255 revised full papers presented were carefully reviewed and selected from 800 submissions in a two-phase review process. The papers have been organized in the following topical sections: Part I: atlas and surface-based techniques; shape and patch-based techniques; registration techniques, functional imaging, connectivity, and brain parcellation; diffusion magnetic resonance imaging (dMRI) and tensor/fiber processing; and image segmentation and modelling. Part II: optical imaging; airway and vessel analysis; motion and cardiac analysis; tumor processing; planning and simulation for medical interventions; interventional imaging and navigation; and medical image computing. Part III: feature extraction and classification techniques; and machine learning in medical image computing.
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
9789814366496
The Pacific Symposium on Biocomputing (PSB) 2012 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in the problems of biological significance. Presentations are rigorously peer-reviewed and are published in an archival proceedings volume. PSB 2012 will be held on January 3 – 7, 2012 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference. PSB 2012 will bring together top researchers from the US, the Asian Pacific nations, and countries around the world to exchange research results and address open issues in all aspects of computational...
Digital Phenomenology
Digital Phenomenology is a report on the philosophical theory of everything. From the first principle, digital philosophy and post-Keynesian economics are proved. The report is technical and aimed toward philosophers, mathematicians, computer scientists, physicists, economists, and political scientists.
A Basic Theory of Everything
What are the basic building blocks of the world? This book presents a naturalistic theory saying that the universe and everything in it can be reduced to three fundamental entities: a field, a set of values that can be actualized at different places in the field, and an actualizer of the values. The theory is defended by using it to answer the main questions in metaphysics, such as: What is causality, existence, laws of nature, consciousness, thinking, free will, time, mathematical entities, ethical values, etc.? The theory is compared with the main alternatives and argued to solve problems better than the existing theories. Several new theories are suggested, such as how to understand mental causation, free will and the truth of ethics and mathematics.
