الصفحة 1
الصفحة 1
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 ; 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part II
The program committee carefully selected 258 revised papers from numerous submissions for presentation in two volumes, based on rigorous peer reviews. The first volume includes 127 papers related to medical image computing, segmentation, shape and statistics analysis, modeling, motion tracking and compensation, as well as registration. The second volume contains 131 contributions related to robotics and interventions, statistical analysis, segmentation, intervention, modeling, and registration.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 ; 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I
The program committee carefully selected 258 revised papers from numerous submissions for presentation in two volumes, based on rigorous peer reviews. The first volume includes 127 papers related to medical image computing, segmentation, shape and statistics analysis, modeling, motion tracking and compensation, as well as registration. The second volume contains 131 contributions related to robotics and interventions, statistical analysis, segmentation, intervention, modeling, and registration.
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006 ; Vol. 4191; 9th International Conference, Copenhagen, Denmark, October 1-6, 2006, Proceedings, Part II
T MICCAI papers are of high standard and have a long lifetime. In this v- ume as well as in the latest journal issues of Medical Image Analysis and IEEE Transactions on Medical Imaging papers cite previous MICCAIs including the ?rst MICCAI conference in Cambridge, Massachusetts, 1998. It is obvious that the community requires the MICCAI papers as archive material. Therefore the proceedingsofMICCAIarefrom2005andhenceforthbeing indexedbyMedline. Acarefulreviewandselectionprocesswasexecutedinordertosecurethebest possible program for the MICCAI 2006 conference. We received 578 scienti?c papers from which 39 papers were selected for the oral program and 193 papers for the poster program.
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006 ; Vol. 4190 ; 9th International Conference, Copenhagen, Denmark, October 1-6, 2006, Proceedings, Part I
MICCAI papers are of high standard and have a long lifetime. In this v- ume as well as in the latest journal issues of Medical Image Analysis and IEEE Transactions on Medical Imaging papers cite previous MICCAIs including the ?rst MICCAI conference in Cambridge, Massachusetts, 1998. It is obvious that the community requires the MICCAI papers as archive material. Therefore the proceedingsofMICCAIarefrom2005andhenceforthbeing indexedbyMedline. Acarefulreviewandselectionprocesswasexecutedinordertosecurethebest possible program for the MICCAI 2006 conference. We received 578 scienti?c papers from which 39 papers were selected for the oral program and 193 papers for the poster program.
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2005 ; 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part II
Robotics, Image-Guided Surgery and Interventions -- Image Registration II -- Medical Image Computing -- Atlases -- Shape I -- Structural and Functional Brain Analysis -- Model-Based Image Analysis -- Image-Guided Intervention: Simulation, Modeling and Display -- Simulation and Modeling II -- Medical Image Computing -- Shape II -- Image Segmentation and Analysis II -- Image Registration III --
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005 ; 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part I
This paper presents a method for classification of medical images, using machine learning and deformation-based morphometry. A morphological representation of the anatomy of interest is first obtained using highdimensional template warping, from which regions that display strong correlations between morphological measurements and the classification (clinical) variable are extracted using a watershed segmentation, taking into account the regional smoothness of the correlation map which is estimated by a crossvalidation strategy in order to achieve robustness to outliers. A Support Vector Machine-Recursive Feature Elimination (SVM-RFE) technique is then used to rank computed features from the extracted regions, according to their effect on the leave-one-out error bound. Finally, SVM classification is applied using the best set of features, and it is tested using leave-one-out. The results from a group of 61 brain images of female normal controls and schizophrenia patients demonstrate not only high classification accuracy (91.8%) and steep ROC curves, but also exceptional stability with respect to the number of selected features and the SVM kernel size
Medical Biometrics ; 1st International Conference, ICMB 2008, Hong Kong, China, January 4-5, 2008, Proceedings
Medical biometrics primarily refers to the usage of beh- ioral and physiological characteristics of humans for medical diagnosis and body care. Thus the goal of medical biometrics is to explore solutions to the open problems in medicine using biometric measurements, technologies and systems.
Human-centered visualization environments : GI-Dagstuhl Research Seminar, Dagstuhl Castle, Germany, March 5-8, 2006, Revised Papers
This tutorial book features an augmented selection of the material presented at the GI-Dagstuhl Research Seminar on Human-Centered Visualization Environments, HCVE 2006, held in Dagstuhl Castle, Germany in March 2006. It presents eight tutorial lectures that are the thoroughly cross-reviewed and revised versions of the summaries and findings presented and discussed at the seminar.
Grid computing in life science ; 1st International Workshop on Life Science Grid, LSGRID 2004 Kanazawa, Japan, May 31-June 1, 2004, Revised Selected and Invited Papers
Researchers in the ?eld of life sciences rely increasingly on information te- nology to extract and manage relevant knowledge. The complex computational and data management needs of life science research make Grid technologies an attractive support solution. However, many important issues must be addressed before the Life Science Grid becomes commonplace. The 1st International Life Science Grid Workshop (LSGRID 2004) was held in Kanazawa Japan, May 31–June 1, 2004. This workshop focused on life s- ence applications of grid systems especially for bionetwork research and systems biology which require heterogeneous data integration from genome to phenome, mathematical modeling and simulation from molecular to population levels, and high-performance computing including parallel processing, special hardware and grid computing.
Database systems for advanced applications ; Vol. 3453 ; 10th international conference, DASFAA 2005, Beijing, China, April 17-20, 2005, Proceedings
Data Stream Mining and Resource Adaptive Computation.- Purpose Based Access Control for Privacy Protection in Database Systems.- Complex Networks and Network Data Mining.- Bioinformatics.- Indexing DNA Sequences Using q-Grams.- PADS: Protein Structure Alignment Using Directional Shape Signatures.- LinkageTracker: A Discriminative Pattern Tracking Approach to Linkage Disequilibrium Mapping.- Watermarking and Encryption.- Query Optimization in Encrypted Database Systems.- Watermarking Spatial Trajectory Database.- Effective Approaches for Watermarking XML Data.- XML Query Processing.- A Unifying Framework for Merging and Evaluating XML Information.- Efficient Evaluation of Partial Match Queries for XML Documents Using Information Retrieval Techniques.- PathStack: A Holistic Path Join Algorithm for Path Query with Not-Predicates on XML Data.- XML Coding and Metadata Management.- An Improved Prefix Labeling Scheme: A Binary String Approach for Dynamic Ordered XML.- Efficiently Coding and Indexing XML Document.- XQuery-Based TV-Anytime Metadata Management.- Data Mining.- Effective Database Transformation and Efficient Support Computation for Mining Sequential Patterns.
Data Mining in Biomedicine
This volume presents an extensive collection of contributions covering aspects of the exciting and important research field of data mining techniques in biomedicine. Coverage includes new approaches for the analysis of biomedical data; applications of data mining techniques to real-life problems in medical practice; comprehensive reviews of recent trends in the field. The book addresses incorporation of data mining in fundamental areas of biomedical research: genomics, proteomics, protein characterization, and neuroscience.
Data Mining for Biomedical Applications ; PAKDD 2006 Workshop, BioDM 2006, Singapore, April 9, 2006, Proceedings
This book constitutes the refereed proceedings of the International Workshop on Data Mining for Biomedical Applications, BioDM 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The 14 revised full papers presented together with 1 keynote talks were carefully reviewed and selected from 35 submissions. The papers are organized in topical sections on protein-protein interactions, database and search, bio data clustering, and in-silico diagnosis.
Data Mining and Bioinformatics ; 1st International Workshop, VDMB 2006, Seoul, Korea, September 11, 2006, Revised Selected Papers
This volume contains the papers presented at the inaugural workshop on Data Mining and Bioinformatics at the 32nd International Conference on Very Large Data Bases (VLDB). The purpose of this workshop was to begin bringing - gether researchersfrom database, data mining, and bioinformatics areas to help leverage respective successes in each to the others.
Data Integration in the Life Sciences ; Vol. 4075 ; 3rd International Workshop, DILS 2006, Hinxton, UK, July 20-22, 2006, Proceedings
Data management and data integration are fundamental problems in the life sciences. Advances in molecular biology and molecular medicine are almost u- versallyunderpinned by enormouse?orts in data management,data integration, automatic data quality assurance, and computational data analysis. Many hot topics in the life sciences, such as systems biology, personalized medicine, and pharmacogenomics, critically depend on integrating data sets and applications producedby di?erent experimentalmethods, in di?erent researchgroups,andat di?erent levels of granularity.
Data integration in the life sciences ; Vol. 3615 ; 2nd international workshop, DILS 2005, San Diego, CA, USA, July 20-22, 2005, Proceedings
Constitutes the refereed proceedings of the Second International Workshop on Data Integration in the Life Sciences, DILS 2005, held in San Diego, CA, USA in July 2005. The papers are organized in sections on user applications, ontologies, data integration, and others, and address the issues in data integration from the life science point of view.
Data Integration in the Life Sciences ; 5th International Workshop, DILS 2008, Evry, France, June 25-27, 2008. Proceedings
This book constitutes the refereed proceedings of the 5th International Workshop on Data Integration in the Life Sciences, DILS 2008, held in Evry, France in June 2008.
Data Integration in the Life Sciences ; 4th International Workshop, DILS 2007, Philadelphia, PA, USA, June 27-29, 2007, Proceedings
it cover a wide spectrum of theoretical and practical issues including scienti?c work?ows, - notation in data integration, mapping and matching techniques, and modeling of life science data. It presenting research on new models, methods, or algorithms and 6 papers presenting imp- mentation of systems or experience with systems in practice.
Computer vision for biomedical image applications
The purpose of this book is to submit the workshop, “Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends” (CVBIA), is to examine the diverse applications of computer vision to biomedical image applications, considering both current methods and promising new trends. An additional goal is to provide the opportunity for direct interactions between (1) prominent senior researchers and young scientists, including students, postdoctoral associates and junior faculty; (2) local researchers and international leaders in biomedical image analysis; and (3) computer scientists and medical practitioners. Our CVBIA workshop had two novel characteristics: each contributed paper was authored primarily by a young scientist, and the workshop attracted an unusually large number of well-respected invited speakers (and their papers).
Computational Life Sciences ; Vol. 4216 ; 2nd International Symposium, CompLife 2006, Cambridge, UK, September 27-29, 2006, Proceedings
This book constitutes the refereed proceedings of the Second International Symposium on Computational Life Sciences, CompLife 2006. The papers are organized in topical sections on genomics, data mining, molecular simulation, molecular informatics, systems biology, biological networks/metabolism, and computational neuroscience.
Machine learning for biomedical application
Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.
