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Computational Diffusion MRI and Brain Connectivity
  • Language: en
  • Pages: 255

Computational Diffusion MRI and Brain Connectivity

This volume contains the proceedings from two closely related workshops: Computational Diffusion MRI (CDMRI’13) and Mathematical Methods from Brain Connectivity (MMBC’13), held under the auspices of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, which took place in Nagoya, Japan, September 2013. Inside, readers will find contributions ranging from mathematical foundations and novel methods for the validation of inferring large-scale connectivity from neuroimaging data to the statistical analysis of the data, accelerated methods for data acquisition, and the most recent developments on mathematical diffusion modeling. This volume offers a valuable starting point for anyone interested in learning computational diffusion MRI and mathematical methods for brain connectivity as well as offers new perspectives and insights on current research challenges for those currently in the field. It will be of interest to researchers and practitioners in computer science, MR physics, and applied mathematics.

Computational Diffusion MRI
  • Language: en
  • Pages: 390

Computational Diffusion MRI

  • Type: Book
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  • Published: 2019-05-17
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  • Publisher: Springer

This volume gathers papers presented at the Workshop on Computational Diffusion MRI (CDMRI’18), which was held under the auspices of the International Conference on Medical Image Computing and Computer Assisted Intervention in Granada, Spain on September 20, 2018. It presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find papers on a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as harmonisation and frontline applications in research and ...

The use of deep learning in mapping and diagnosis of cancers
  • Language: en
  • Pages: 228

The use of deep learning in mapping and diagnosis of cancers

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Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2009
  • Language: en
  • Pages: 1037

Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2009

  • Type: Book
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  • Published: 2009-10-01
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  • Publisher: Springer

The two-volume set LNCS 5761 and LNCS 5762 constitute the refereed proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009, held in London, UK, in September 2009. Based on rigorous peer reviews, the program committee carefully selected 259 revised papers from 804 submissions for presentation in two volumes. The first volume includes 125 papers divided in topical sections on cardiovascular image guided intervention and robotics; surgical navigation and tissue interaction; intra-operative imaging and endoscopic navigation; motion modelling and image formation; image registration; modelling and segmentation; image segmentation and classification; segmentation and atlas based techniques; neuroimage analysis; surgical navigation and robotics; image registration; and neuroimage analysis: structure and function.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
  • Language: en
  • Pages: 849

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...

Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis
  • Language: en
  • Pages: 166

Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis

  • Type: Book
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  • Published: 2017-09-06
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  • Publisher: Springer

This book constitutes the refereed joint proceedings of the 6th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2017, and the Second International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 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 6 full papers presented at CVII-STENT 2017 and the 11 full papers presented at LABELS 2017 were carefully reviewed and selected. The CVII-STENT papers feature the state of the art in imaging, treatment, and computer-assisted intervention in the field of endovascular interventions. The LABELS papers present a variety of approaches for dealing with few labels, from transfer learning to crowdsourcing.

Breast Imaging
  • Language: en
  • Pages: 787

Breast Imaging

  • Type: Book
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  • Published: 2014-06-23
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  • Publisher: Springer

This book constitutes the refereed proceedings of the 12th International Workshop on Breast Imaging, IWDM 2014, held in Gifu City, Japan, in June/July 2014. The 24 revised full papers and 73 revised poster papers presented together with 6 invited talks were carefully reviewed and selected from 122 submissions. The papers are organized in topical sections on screening outcomes, ultrasound, breast density, imaging physics, CAD, tomosynthesis and ICT and image processing.

Machine Learning in Medical Imaging
  • Language: en
  • Pages: 343

Machine Learning in Medical Imaging

  • Type: Book
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  • Published: 2014-09-05
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  • Publisher: Springer

This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Medical Imaging, MLMI 2014, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014, in Cambridge, MA, USA, in September 2014. The 40 contributions included in this volume were carefully reviewed and selected from 70 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 Image Computing and Computer-Assisted Intervention -- MICCAI 2010
  • Language: en
  • Pages: 704

Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2010

  • Type: Book
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  • Published: 2010-09-21
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  • Publisher: Springer

The13thInternationalConferenceonMedicalImageComputingandComputer- Assisted Intervention, MICCAI 2010, was held in Beijing, China from 20-24 September,2010.ThevenuewastheChinaNationalConventionCenter(CNCC), China’slargestandnewestconferencecenterwith excellentfacilities andaprime location in the heart of the Olympic Green, adjacent to characteristic constr- tions like the Bird’s Nest (National Stadium) and the Water Cube (National Aquatics Center). MICCAI is the foremost international scienti?c event in the ?eld of medical image computing and computer-assisted interventions. The annual conference has a high scienti?c standard by virtue of the threshold for acceptance, and accordingly MICC...

Machine Learning in Medical Imaging
  • Language: en
  • Pages: 422

Machine Learning in Medical Imaging

  • Type: Book
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  • Published: 2018-09-14
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  • Publisher: Springer

This book constitutes the proceedings of the 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018. The 45 papers presented in this volume were carefully reviewed and selected from 82 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.