|
Showing 1 - 17 of
17 matches in All Departments
Deep Learning for Medical Image Analysis, Second Edition is a great
learning resource for academic and industry researchers and
graduate students taking courses on machine learning and deep
learning for computer vision and medical image computing and
analysis. Deep learning provides exciting solutions for medical
image analysis problems and is a key method for future
applications. This book gives a clear understanding of the
principles and methods of neural network and deep learning
concepts, showing how the algorithms that integrate deep learning
as a core component are applied to medical image detection,
segmentation, registration, and computer-aided analysis.
|
Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Proceedings (Paperback, 2013 ed.)
Guorong Wu, Daoqiang Zhang, Dinggang Shen, Pingkun Yan, Kenji Suzuki, …
|
R1,557
Discovery Miles 15 570
|
Ships in 10 - 15 working days
|
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.
|
Multimodal Brain Image Analysis - Third International Workshop, MBIA 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Proceedings (Paperback, 2013)
Li Shen, Tianming Liu, Pew-Thian Yap, Heng Huang, Dinggang Shen, …
|
R1,557
Discovery Miles 15 570
|
Ships in 10 - 15 working days
|
This book constitutes the refereed proceedings of the Third
International Workshop on Multimodal Brain Image Analysis, MBIA
2013, held in Nagoya, Japan, on September 22, 2013 in conjunction
with the 16th International Conference on Medical Image Computing
and Computer Assisted Intervention, MICCAI. The 24 revised full
papers presented were carefully reviewed and selected from 35
submissions. The papers are organized in topical sections on
analysis, methodologies, algorithms, software systems, validation
approaches, benchmark datasets, neuroscience and clinical
applications.
|
Multimodal Brain Image Analysis - Second International Workshop, MBIA 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1-5, 2012, Proceedings (Paperback, 2012 ed.)
Pew-Thian Yap, Tianming Liu, Dinggang Shen, Carl-Fredrik Westin, Li Shen
|
R1,990
Discovery Miles 19 900
|
Ships in 10 - 15 working days
|
This book constitutes the refereed proceedings of the Second
International Workshop on Multimodal Brain Image Analysis, held in
conjunction with MICCAI 2012, in Nice, France, in October 2012. The
19 revised full papers presented were carefully reviewed and
selected from numerous submissions. The objective of this workshop
is to forward the state of the art in analysis methodologies,
algorithms, software systems, validation approaches, benchmark
datasets, neuroscience, and clinical applications.
|
Multimodal Brain Image Analysis - First International Workshop, MBIA 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011, Proceedings (Paperback, Edition.)
Tianming Liu, Dinggang Shen, Luis Ibanez, Xiaodong Tao
|
R1,901
Discovery Miles 19 010
|
Ships in 10 - 15 working days
|
This book constitutes the refereed proceedings of the First
International Workshop on Multimodal Brain Image Analysis, held in
conjunction with MICCAI 2011, in Toronto, Canada, in September
2011.
The 15 revised full papers presented together with 4 poster papers
were carefully reviewed and selected from 24 submissions. The
objective of this workshop is to facilitate advancements in the
multimodal brain image analysis field, in terms of analysis
methodologies, algorithms, software systems, validation approaches,
benchmark datasets, neuroscience, and clinical applications.
|
Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011, Proceedings (Paperback)
Kenji Suzuki, Fei Wang, Dinggang Shen, Pingkun Yan
|
R1,585
Discovery Miles 15 850
|
Ships in 10 - 15 working days
|
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.
|
Machine Learning in Medical Imaging - First International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010, Proceedings (Paperback, Edition.)
Fei Wang, Pingkun Yan, Kenji Suzuki, Dinggang Shen
|
R1,557
Discovery Miles 15 570
|
Ships in 10 - 15 working days
|
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.
|
Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 - 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part III (Paperback, 1st ed. 2019)
Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, …
|
R3,157
Discovery Miles 31 570
|
Ships in 10 - 15 working days
|
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and
11769 constitutes the refereed proceedings of the 22nd
International Conference on Medical Image Computing and
Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen,
China, in October 2019. The 539 revised full papers presented were
carefully reviewed and selected from 1730 submissions in a
double-blind review process. The papers are organized in the
following topical sections: Part I: optical imaging; endoscopy;
microscopy. Part II: image segmentation; image registration;
cardiovascular imaging; growth, development, atrophy and
progression. Part III: neuroimage reconstruction and synthesis;
neuroimage segmentation; diffusion weighted magnetic resonance
imaging; functional neuroimaging (fMRI); miscellaneous
neuroimaging. Part IV: shape; prediction; detection and
localization; machine learning; computer-aided diagnosis; image
reconstruction and synthesis. Part V: computer assisted
interventions; MIC meets CAI. Part VI: computed tomography; X-ray
imaging.
|
Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 - 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part IV (Paperback, 1st ed. 2019)
Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, …
|
R1,731
Discovery Miles 17 310
|
Ships in 10 - 15 working days
|
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and
11769 constitutes the refereed proceedings of the 22nd
International Conference on Medical Image Computing and
Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen,
China, in October 2019. The 539 revised full papers presented were
carefully reviewed and selected from 1730 submissions in a
double-blind review process. The papers are organized in the
following topical sections: Part I: optical imaging; endoscopy;
microscopy. Part II: image segmentation; image registration;
cardiovascular imaging; growth, development, atrophy and
progression. Part III: neuroimage reconstruction and synthesis;
neuroimage segmentation; diffusion weighted magnetic resonance
imaging; functional neuroimaging (fMRI); miscellaneous
neuroimaging. Part IV: shape; prediction; detection and
localization; machine learning; computer-aided diagnosis; image
reconstruction and synthesis. Part V: computer assisted
interventions; MIC meets CAI. Part VI: computed tomography; X-ray
imaging.
|
Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 - 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part V (Paperback, 1st ed. 2019)
Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, …
|
R1,695
Discovery Miles 16 950
|
Ships in 10 - 15 working days
|
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and
11769 constitutes the refereed proceedings of the 22nd
International Conference on Medical Image Computing and
Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen,
China, in October 2019. The 539 revised full papers presented were
carefully reviewed and selected from 1730 submissions in a
double-blind review process. The papers are organized in the
following topical sections: Part I: optical imaging; endoscopy;
microscopy. Part II: image segmentation; image registration;
cardiovascular imaging; growth, development, atrophy and
progression. Part III: neuroimage reconstruction and synthesis;
neuroimage segmentation; diffusion weighted magnetic resonance
imaging; functional neuroimaging (fMRI); miscellaneous
neuroimaging. Part IV: shape; prediction; detection and
localization; machine learning; computer-aided diagnosis; image
reconstruction and synthesis. Part V: computer assisted
interventions; MIC meets CAI. Part VI: computed tomography; X-ray
imaging.
|
Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 - 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part I (Paperback, 1st ed. 2019)
Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, …
|
R3,135
Discovery Miles 31 350
|
Ships in 10 - 15 working days
|
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and
11769 constitutes the refereed proceedings of the 22nd
International Conference on Medical Image Computing and
Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen,
China, in October 2019. The 539 revised full papers presented were
carefully reviewed and selected from 1730 submissions in a
double-blind review process. The papers are organized in the
following topical sections: Part I: optical imaging; endoscopy;
microscopy. Part II: image segmentation; image registration;
cardiovascular imaging; growth, development, atrophy and
progression. Part III: neuroimage reconstruction and synthesis;
neuroimage segmentation; diffusion weighted magnetic resonance
imaging; functional neuroimaging (fMRI); miscellaneous
neuroimaging. Part IV: shape; prediction; detection and
localization; machine learning; computer-aided diagnosis; image
reconstruction and synthesis. Part V: computer assisted
interventions; MIC meets CAI. Part VI: computed tomography; X-ray
imaging.
|
Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 - 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part II (Paperback, 1st ed. 2019)
Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, …
|
R3,152
Discovery Miles 31 520
|
Ships in 10 - 15 working days
|
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and
11769 constitutes the refereed proceedings of the 22nd
International Conference on Medical Image Computing and
Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen,
China, in October 2019. The 539 revised full papers presented were
carefully reviewed and selected from 1730 submissions in a
double-blind review process. The papers are organized in the
following topical sections: Part I: optical imaging; endoscopy;
microscopy. Part II: image segmentation; image registration;
cardiovascular imaging; growth, development, atrophy and
progression. Part III: neuroimage reconstruction and synthesis;
neuroimage segmentation; diffusion weighted magnetic resonance
imaging; functional neuroimaging (fMRI); miscellaneous
neuroimaging. Part IV: shape; prediction; detection and
localization; machine learning; computer-aided diagnosis; image
reconstruction and synthesis. Part V: computer assisted
interventions; MIC meets CAI. Part VI: computed tomography; X-ray
imaging.
|
Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 - 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part VI (Paperback, 1st ed. 2019)
Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, …
|
R3,148
Discovery Miles 31 480
|
Ships in 10 - 15 working days
|
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and
11769 constitutes the refereed proceedings of the 22nd
International Conference on Medical Image Computing and
Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen,
China, in October 2019. The 539 revised full papers presented were
carefully reviewed and selected from 1730 submissions in a
double-blind review process. The papers are organized in the
following topical sections: Part I: optical imaging; endoscopy;
microscopy. Part II: image segmentation; image registration;
cardiovascular imaging; growth, development, atrophy and
progression. Part III: neuroimage reconstruction and synthesis;
neuroimage segmentation; diffusion weighted magnetic resonance
imaging; functional neuroimaging (fMRI); miscellaneous
neuroimaging. Part IV: shape; prediction; detection and
localization; machine learning; computer-aided diagnosis; image
reconstruction and synthesis. Part V: computer assisted
interventions; MIC meets CAI. Part VI: computed tomography; X-ray
imaging.
Deep learning is providing exciting solutions for medical image
analysis problems and is seen as a key method for future
applications. This book gives a clear understanding of the
principles and methods of neural network and deep learning
concepts, showing how the algorithms that integrate deep learning
as a core component have been applied to medical image detection,
segmentation and registration, and computer-aided analysis, using a
wide variety of application areas. Deep Learning for Medical Image
Analysis is a great learning resource for academic and industry
researchers in medical imaging analysis, and for graduate students
taking courses on machine learning and deep learning for computer
vision and medical image computing and analysis.
|
Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings (Paperback, 1st ed. 2017)
Marc Niethammer, Martin Styner, Stephen Aylward, Hongtu Zhu, Ipek Oguz, …
|
R1,686
Discovery Miles 16 860
|
Ships in 10 - 15 working days
|
This book constitutes the proceedings of the 25th International
Conference on Information Processing in Medical Imaging, IPMI 2017,
held at the Appalachian State University, Boon, NC, USA, in June
2017. The 53 full papers presented in this volume were carefully
reviewed and selected from 147 submissions. They were organized in
topical sections named: analysis on manifolds; shape analysis;
disease diagnosis/progression; brain networks an connectivity;
diffusion imaging; quantitative imaging; imaging genomics; image
registration; segmentation; general image analysis.
|
Machine Learning in Medical Imaging - Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012, Revised Selected Papers (Paperback, 2012 ed.)
Fei Wang, Dinggang Shen, Pingkun Yan, Kenji Suzuki
|
R1,429
Discovery Miles 14 290
|
Ships in 10 - 15 working days
|
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.
Machine Learning and Medical Imaging presents state-of- the-art
machine learning methods in medical image analysis. It first
summarizes cutting-edge machine learning algorithms in medical
imaging, including not only classical probabilistic modeling and
learning methods, but also recent breakthroughs in deep learning,
sparse representation/coding, and big data hashing. In the second
part leading research groups around the world present a wide
spectrum of machine learning methods with application to different
medical imaging modalities, clinical domains, and organs. The
biomedical imaging modalities include ultrasound, magnetic
resonance imaging (MRI), computed tomography (CT), histology, and
microscopy images. The targeted organs span the lung, liver, brain,
and prostate, while there is also a treatment of examining genetic
associations. Machine Learning and Medical Imaging is an ideal
reference for medical imaging researchers, industry scientists and
engineers, advanced undergraduate and graduate students, and
clinicians.
|
You may like...
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
|