As market competition replaces state regulation in many economic fields, competition policy has become an area of increasing significance. Against this background, Suzuki highlights the importance of the domestic political structure for competition policy. He does this through the comparative analysis of competition law reforms in Britain and Japan. He argues - controversially - that a country's domestic political structure should be considered a major factor in causing the reform of competition law, and rejects the established view that it is necessarily a result of changes in international economic and political conditions.

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This book offers the first comprehensive overview of artificial intelligence (AI) technologies in decision support systems for diagnosis based on medical images, presenting cutting-edge insights from thirteen leading research groups around the world. Medical imaging offers essential information on patients' medical condition, and clues to causes of their symptoms and diseases. Modern imaging modalities, however, also produce a large number of images that physicians have to accurately interpret. This can lead to an "information overload" for physicians, and can complicate their decision-making. As such, intelligent decision support systems have become a vital element in medical-image-based diagnosis and treatment. Presenting extensive information on this growing field of AI, the book offers a valuable reference guide for professors, students, researchers and professionals who want to learn about the most recent developments and advances in the field.

Computational Intelligence in Biomedical Imaging is a comprehensive overview of the state-of-the-art computational intelligence research and technologies in biomedical images with emphasis on biomedical decision making. Biomedical imaging offers useful information on patients' medical conditions and clues to causes of their symptoms and diseases. Biomedical images, however, provide a large number of images which physicians must interpret. Therefore, computer aids are demanded and become indispensable in physicians' decision making. This book discusses major technical advancements and research findings in the field of computational intelligence in biomedical imaging, for example, computational intelligence in computer-aided diagnosis for breast cancer, prostate cancer, and brain disease, in lung function analysis, and in radiation therapy. The book examines technologies and studies that have reached the practical level, and those technologies that are becoming available in clinical practices in hospitals rapidly such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy.

This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, in Nagoya, Japan, in September 2013. The 32 contributions included in this volume were carefully reviewed and selected from 57 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.

Medical imaging is an indispensable tool for modern healthcare. Machine leaning plays an essential role in the medical imaging field, with applications including medical image analysis, computer-aided diagnosis, organ/lesion segmentation, image fusion, image-guided therapy, and image annotation and image retrieval. Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis provides a comprehensive overview of machine learning research and technology in medical decision-making based on medical images. This book covers major technical advancements and research findings in the field of Computer-Aided Diagnosis (CAD). As it demonstrates the practical applications of CAD, this book is a useful reference for professors in engineering and medical schools, students in engineering and applied-science, medical students, medical engineers, researchers in industry, academia, and health science, radiologists, cardiologists, surgeons, and healthcare professionals.

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning in Medical Imaging, MLMI 2011, held in conjunction with MICCAI 2011, in Toronto, Canada, in September 2011. The 44 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers focus on major trends in machine learning in medical imaging aiming to identify new cutting-edge techniques and their use in medical imaging.

The first International Workshop on Machine Learning in Medical Imaging, MLMI 2010, was held at the China National Convention Center, Beijing, China on Sept- ber 20, 2010 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2010. Machine learning plays an essential role in the medical imaging field, including image segmentation, image registration, computer-aided diagnosis, image fusion, ima- guided therapy, image annotation, and image database retrieval. With advances in me- cal imaging, new imaging modalities, and methodologies such as cone-beam/multi-slice CT, 3D Ultrasound, tomosynthesis, diffusion-weighted MRI, electrical impedance to- graphy, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Single-sample evidence provided by the patient's imaging data is often not sufficient to provide satisfactory performance; the- fore tasks in medical imaging require learning from examples to simulate a physician's prior knowledge of the data. The MLMI 2010 is the first workshop on this topic. The workshop focuses on major trends and challenges in this area, and works to identify new techniques and their use in medical imaging. Our goal is to help advance the scientific research within the broad field of medical imaging and machine learning. The range and level of submission for this year's meeting was of very high quality. Authors were asked to submit full-length papers for review. A total of 38 papers were submitted to the workshop in response to the call for papers.

This volume contains the proceedings of TESTCOM/FATES 2008, a joint c- ference of two communities: TESTCOM was the 20th edition of the IFIP TC6/ WG6.1 International Conference on Testing of Communicating Systems and FATESwasthe8theditionoftheInternationalWorkshoponFormalApproaches to Testing of Software. TESTCOM/FATES 2008 was held at the Campus In- vation Center in Tokyo, Japan during June 10-13, 2008. Testing is one of the most important techniques for validating and che- ing the correctness of communication and software systems. Testing, however, is also a laborious and very cost-intensive task during the development process of such systems. TESTCOM is a series of international conferences addressing the problemsoftestingcommunicatingsystems, including communicationprotocols, services, distributed platforms, and middleware. FATES is a series of inter- tional workshopsdiscussing the challengesof using rigorousand formal methods for testing software systems in general. TESTCOM/FATES aims at being a - rum for researchers, developers, and testers to review, discuss, and learn about new approaches, concepts, theories, methodologies, tools, and experiences in the ?eld of testing of communicating systems and software. TESTCOM has a long history. Previously it was called the International Workshop on Protocol Test Systems (IWPTS) and changed its name to the - ternationalWorkshoponTestingofCommunicatingSystem(IWTCS)later.The previous conferences were held in Vancouver, Canada (1988); Berlin, Germany (1989);McLean, USA (1990);Leidschendam, TheNetherlands(1991);Montr eal, Canada (1992); Pau, France (1993); Tokyo, Japan (1994); Evry, France (1995); Darmstadt, Germany(1996);Cheju Island, Korea(1997);Tomsk, Russia(1998); Budapest, Hungary (1999); Ottawa, Canada (2000); Berlin, Germany (2001); Sophia Antipolis, France (2002); Oxford, UK (2004); Montr eal, Canada (2005); New York, USA (2006) and Tallinn, Estonia (200

This volume contains the proceedings of FORTE 2008, 28th IFIP WG6.1 - ternational Conference on Formal Techniques for Networked and Distributed Systems. FORTE 2008 was held at the Campus Innovation Center in Tokyo, Japan during June 10-13, 2008. FORTE denotes a series of international wo- ing conferences on formal description techniques applied to computer networks and distributed systems. The conference series started in 1981 under the name PSTV. In 1988 a second series under the name FORTE was set up. Both - ries were united to FORTE/PSTV in 1996. In 2001 the conference changed the name to its current form. Recent conferences of this long series were held in Berlin (2003), Madrid(2004), Taipei(2005), Paris(2006), and Tallinn(2007). As in the previous year, FORTE 2008 was collocated with TESTCOM/ FATES 2008: the 20th IFIP International Conference on Testing of Com- nicating Systems (TESTCOM) and the 8th International Workshop on Formal Approaches to Testing of Software (FATES). The co-location of FORTE and TESTCOM/FATES fostered the collaboration between their communities. The commonspiritofboth conferenceswasunderpinnedby jointopening andclosing sessions, invited talks, as well as joint social events.

This book offers the first comprehensive overview of artificial intelligence (AI) technologies in decision support systems for diagnosis based on medical images, presenting cutting-edge insights from thirteen leading research groups around the world. Medical imaging offers essential information on patients' medical condition, and clues to causes of their symptoms and diseases. Modern imaging modalities, however, also produce a large number of images that physicians have to accurately interpret. This can lead to an "information overload" for physicians, and can complicate their decision-making. As such, intelligent decision support systems have become a vital element in medical-image-based diagnosis and treatment. Presenting extensive information on this growing field of AI, the book offers a valuable reference guide for professors, students, researchers and professionals who want to learn about the most recent developments and advances in the field.

This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.

This book constitutes the refereed proceedings of the 9th International Conference on Social Robotics, ICSR 2016, held in Tsukuba, Japan, in November 2017.The 74 revised full papers presented were carefully reviewed and selected from 110 submissions. The theme of the 2017 conference is: Embodied Interactive Robots. In addition to the technical sessions, ICSR 2017 included four workshops: 1) Social Robot Intelligence for Social Human-Robot Interaction of Service Robots; 2) Human Safety and Comfort in Human-Robot Interactive Social Environments; 3) Modes of Interaction for Social Robots (MISR 2017): Postures, Gestures and Microinteractions; and 4) Religion in Robotics.

This book constitutes the refereed proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.

Cybernics plays a significant role in coping with an aging society using state-of-the-art technologies from engineering, clinical medicine and humanities. This new interdisciplinary field studies technologies that enhance, strengthen, and support physical and cognitive functions of human beings, based on the fusion of human, machine, and information systems. The design of a seamless interface for interaction between the interior and exterior of the human body is described in this book from diverse aspects such as the physical, neurophysiological, and cognitive levels. It is the first book to cover the many aspects of cybernics, allowing readers to understand the life support robotics technology for the elderly, including remote, in-home, hospital, institutional, community medical welfare, and vital-sensing systems. Serving as a valuable resource, this volume will interest not only graduate students, scientists, and engineers but also newcomers to the field of cybernics.

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Medical Imaging, MLMI 2012, held in conjunction with MICCAI 2012, in Nice, France, in October 2012. The 33 revised full papers presented were carefully reviewed and selected from 67 submissions. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. It focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging.