Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging And Graphs In Biomedical Image Analysis

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

Carole H. Sudre,Hamid Fehri,Tal Arbel,Christian F. Baumgartner,Adrian Dalca,Ryutaro Tanno,Koen Van Leemput,William M. Wells,Aristeidis Sotiras,Bartlomiej Papiez,Enzo Ferrante,Sarah Parisot
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Graphs in Biomedical Image Analysis Book PDF

Author : Carole H. Sudre,Hamid Fehri,Tal Arbel,Christian F. Baumgartner,Adrian Dalca,Ryutaro Tanno,Koen Van Leemput,William M. Wells,Aristeidis Sotiras,Bartlomiej Papiez,Enzo Ferrante,Sarah Parisot
Publisher : Springer Nature
Page : 233 pages
File Size : 44,7 Mb
Release : 2020-10-05
Category : Computers
ISBN : 9783030603656

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis by Carole H. Sudre,Hamid Fehri,Tal Arbel,Christian F. Baumgartner,Adrian Dalca,Ryutaro Tanno,Koen Van Leemput,William M. Wells,Aristeidis Sotiras,Bartlomiej Papiez,Enzo Ferrante,Sarah Parisot Pdf

This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures

Hayit Greenspan,Ryutaro Tanno,Marius Erdt,Tal Arbel,Christian Baumgartner,Adrian Dalca,Carole H. Sudre,William M. Wells,Klaus Drechsler,Marius George Linguraru,Cristina Oyarzun Laura,Raj Shekhar,Stefan Wesarg,Miguel Ángel González Ballester
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image Based Procedures Book PDF

Author : Hayit Greenspan,Ryutaro Tanno,Marius Erdt,Tal Arbel,Christian Baumgartner,Adrian Dalca,Carole H. Sudre,William M. Wells,Klaus Drechsler,Marius George Linguraru,Cristina Oyarzun Laura,Raj Shekhar,Stefan Wesarg,Miguel Ángel González Ballester
Publisher : Springer Nature
Page : 192 pages
File Size : 42,5 Mb
Release : 2019-10-10
Category : Computers
ISBN : 9783030326890

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures by Hayit Greenspan,Ryutaro Tanno,Marius Erdt,Tal Arbel,Christian Baumgartner,Adrian Dalca,Carole H. Sudre,William M. Wells,Klaus Drechsler,Marius George Linguraru,Cristina Oyarzun Laura,Raj Shekhar,Stefan Wesarg,Miguel Ángel González Ballester Pdf

This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.

Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis

Luigi Manfredi,Seyed-Ahmad Ahmadi,Michael Bronstein,Anees Kazi,Davide Lomanto,Alwyn Mathew,Ludovic Magerand,Kamilia Mullakaeva,Bartlomiej Papiez,Russell H. Taylor,Emanuele Trucco
Imaging Systems for GI Endoscopy and Graphs in Biomedical Image Analysis Book PDF

Author : Luigi Manfredi,Seyed-Ahmad Ahmadi,Michael Bronstein,Anees Kazi,Davide Lomanto,Alwyn Mathew,Ludovic Magerand,Kamilia Mullakaeva,Bartlomiej Papiez,Russell H. Taylor,Emanuele Trucco
Publisher : Springer Nature
Page : 138 pages
File Size : 54,5 Mb
Release : 2022-12-09
Category : Computers
ISBN : 9783031210839

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Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis by Luigi Manfredi,Seyed-Ahmad Ahmadi,Michael Bronstein,Anees Kazi,Davide Lomanto,Alwyn Mathew,Ludovic Magerand,Kamilia Mullakaeva,Bartlomiej Papiez,Russell H. Taylor,Emanuele Trucco Pdf

This book constitutes the refereed proceedings of the first MICCAI Workshop, ISGIE 2022, Imaging Systems for GI Endoscopy, and the Fourth MICCAI Workshop, GRAIL 2022, GRaphs in biomedicAL Image and analysis, held in conjunction with MICCAI 2022, Singapore, September 18, 2022. ISGIE 2022 accepted 6 papers from the 8 submissions received.This workshop focuses on novel scientific contributions to vision systems, imaging algorithms as well as the autonomous system for endorobot for GI endoscopy. This includes lesion and lumen detection, as well as 3D reconstruction of the GI tract and hand-eye coordination. GRAIL 2022 accepted 6 papers from the 10 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

Carole H. Sudre,Christian F. Baumgartner,Adrian Dalca,Raghav Mehta,Chen Qin,William M. Wells
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging Book PDF

Author : Carole H. Sudre,Christian F. Baumgartner,Adrian Dalca,Raghav Mehta,Chen Qin,William M. Wells
Publisher : Springer Nature
Page : 232 pages
File Size : 48,9 Mb
Release : 2023-10-06
Category : Computers
ISBN : 9783031443367

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging by Carole H. Sudre,Christian F. Baumgartner,Adrian Dalca,Raghav Mehta,Chen Qin,William M. Wells Pdf

This book constitutes the refereed proceedings of the 5th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2023, held in conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. For this workshop, 21 papers from 32 submissions were accepted for publication. The accepted papers cover the fields of uncertainty estimation and modeling, as well as out of distribution management, domain shift robustness, Bayesian deep learning and uncertainty calibration.

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis

Carole H. Sudre,Roxane Licandro,Christian Baumgartner,Andrew Melbourne,Adrian Dalca,Jana Hutter,Ryutaro Tanno,Esra Abaci Turk,Koen Van Leemput,Jordina Torrents Barrena,William M. Wells,Christopher Macgowan
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Perinatal Imaging Placental and Preterm Image Analysis Book PDF

Author : Carole H. Sudre,Roxane Licandro,Christian Baumgartner,Andrew Melbourne,Adrian Dalca,Jana Hutter,Ryutaro Tanno,Esra Abaci Turk,Koen Van Leemput,Jordina Torrents Barrena,William M. Wells,Christopher Macgowan
Publisher : Springer Nature
Page : 306 pages
File Size : 44,8 Mb
Release : 2021-09-30
Category : Computers
ISBN : 9783030877354

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis by Carole H. Sudre,Roxane Licandro,Christian Baumgartner,Andrew Melbourne,Adrian Dalca,Jana Hutter,Ryutaro Tanno,Esra Abaci Turk,Koen Van Leemput,Jordina Torrents Barrena,William M. Wells,Christopher Macgowan Pdf

This book constitutes the refereed proceedings of the Third Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2021, 13 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. PIPPI 2021 accepted 14 papers from the 18 submissions received. The workshop aims to bring together methods and experience from researchers and authors working on these younger cohorts and provides a forum for the open discussion of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period.

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

Carole H. Sudre,Christian F. Baumgartner,Adrian Dalca,Chen Qin,Ryutaro Tanno,Koen Van Leemput,William M. Wells III
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging Book PDF

Author : Carole H. Sudre,Christian F. Baumgartner,Adrian Dalca,Chen Qin,Ryutaro Tanno,Koen Van Leemput,William M. Wells III
Publisher : Springer Nature
Page : 152 pages
File Size : 55,6 Mb
Release : 2022-09-17
Category : Computers
ISBN : 9783031167492

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Uncertainty for Safe Utilization of Machine Learning in Medical Imaging by Carole H. Sudre,Christian F. Baumgartner,Adrian Dalca,Chen Qin,Ryutaro Tanno,Koen Van Leemput,William M. Wells III Pdf

This book constitutes the refereed proceedings of the Fourth Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2022, held in conjunction with MICCAI 2022. The conference was hybrid event held from Singapore. For this workshop, 13 papers from 22 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world.

Artificial Intelligence Applications In Human Pathology

Ralf Huss,Michael Grunkin
Artificial Intelligence Applications In Human Pathology Book PDF

Author : Ralf Huss,Michael Grunkin
Publisher : World Scientific
Page : 337 pages
File Size : 47,5 Mb
Release : 2022-03-04
Category : Science
ISBN : 9781800611405

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Artificial Intelligence Applications In Human Pathology by Ralf Huss,Michael Grunkin Pdf

Artificial Intelligence Applications in Human Pathology deals with the latest topics in biomedical research and clinical cancer diagnostics. With chapters provided by true international experts in the field, this book gives real examples of the implementation of AI and machine learning in human pathology.Advances in machine learning and AI in general have propelled computational and general pathology research. Today, computer systems approach the diagnostic levels achieved by humans for certain well-defined tasks in pathology. At the same time, pathologists are faced with an increased workload both quantitatively (numbers of cases) and qualitatively (the amount of work per case, with increasing treatment options and the type of data delivered by pathologists also expected to become more fine-grained). AI will support and leverage mathematical tools and implement data-driven methods as a center for data interpretation in modern tissue diagnosis and pathology. Digital or computational pathology will also foster the training of future computational pathologists, those with both pathology and non-pathology backgrounds, who will eventually decide that AI-based pathology will serve as an indispensable hub for data-related research in a global health care system.Some of the specific topics explored within include an introduction to DL as applied to Pathology, Standardized Tissue Sampling for Automated Analysis, integrating Computational Pathology into Histopathology workflows. Readers will also find examples of specific techniques applied to specific diseases that will aid their research and treatments including but not limited to; Tissue Cartography for Colorectal Cancer, Ki-67 Measurements in Breast Cancer, and Light-Sheet Microscopy as applied to Virtual Histology.The key role for pathologists in tissue diagnostics will prevail and even expand through interdisciplinary work and the intuitive use of an advanced and interoperating (AI-supported) pathology workflow delivering novel and complex features that will serve the understanding of individual diseases and of course the patient.

Deep Learning for Medical Image Analysis

S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Deep Learning for Medical Image Analysis Book PDF

Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publisher : Academic Press
Page : 544 pages
File Size : 51,7 Mb
Release : 2023-12-01
Category : Computers
ISBN : 9780323858885

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Deep Learning for Medical Image Analysis by S. Kevin Zhou,Hayit Greenspan,Dinggang Shen Pdf

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. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache

Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques

Gonzalez, Fabio A.,Romero, Eduardo
Biomedical Image Analysis and Machine Learning Technologies Applications and Techniques Book PDF

Author : Gonzalez, Fabio A.,Romero, Eduardo
Publisher : IGI Global
Page : 390 pages
File Size : 51,9 Mb
Release : 2009-12-31
Category : Computers
ISBN : 9781605669571

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Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques by Gonzalez, Fabio A.,Romero, Eduardo Pdf

Medical images are at the base of many routine clinical decisions and their influence continues to increase in many fields of medicine. Since the last decade, computers have become an invaluable tool for supporting medical image acquisition, processing, organization and analysis. Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques provides a panorama of the current boundary between biomedical complexity coming from the medical image context and the multiple techniques which have been used for solving many of these problems. This innovative publication serves as a leading industry reference as well as a source of creative ideas for applications of medical issues.

Machine Learning and Medical Imaging

Guorong Wu,Dinggang Shen,Mert Sabuncu
Machine Learning and Medical Imaging Book PDF

Author : Guorong Wu,Dinggang Shen,Mert Sabuncu
Publisher : Academic Press
Page : 512 pages
File Size : 43,6 Mb
Release : 2016-08-11
Category : Technology & Engineering
ISBN : 9780128041147

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Machine Learning and Medical Imaging by Guorong Wu,Dinggang Shen,Mert Sabuncu Pdf

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. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques

Machine Learning in Medical Imaging

Kenji Suzuki,Fei Wang,Dinggang Shen,Pingkun Yan
Machine Learning in Medical Imaging Book PDF

Author : Kenji Suzuki,Fei Wang,Dinggang Shen,Pingkun Yan
Publisher : Springer
Page : 371 pages
File Size : 52,6 Mb
Release : 2011-09-25
Category : Computers
ISBN : 9783642243196

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Machine Learning in Medical Imaging by Kenji Suzuki,Fei Wang,Dinggang Shen,Pingkun Yan Pdf

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.

Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities

Danail Stoyanov,Zeike Taylor,Enzo Ferrante,Adrian V. Dalca,Anne Martel,Lena Maier-Hein,Sarah Parisot,Aristeidis Sotiras,Bartlomiej Papiez,Mert R. Sabuncu,Li Shen
Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non Imaging Modalities Book PDF

Author : Danail Stoyanov,Zeike Taylor,Enzo Ferrante,Adrian V. Dalca,Anne Martel,Lena Maier-Hein,Sarah Parisot,Aristeidis Sotiras,Bartlomiej Papiez,Mert R. Sabuncu,Li Shen
Publisher : Springer
Page : 101 pages
File Size : 42,5 Mb
Release : 2018-09-15
Category : Computers
ISBN : 9783030006891

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Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities by Danail Stoyanov,Zeike Taylor,Enzo Ferrante,Adrian V. Dalca,Anne Martel,Lena Maier-Hein,Sarah Parisot,Aristeidis Sotiras,Bartlomiej Papiez,Mert R. Sabuncu,Li Shen Pdf

This book constitutes the refereed joint proceedings of the Second International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and the First International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 6 full papers presented at GRAIL 2018 and the 5 full papers presented at BeYond MIC 2018 were carefully reviewed and selected. The GRAIL papers cover a wide range of develop graph-based models for the analysis of biomedical images and encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts. The Beyond MIC papers cover topics of novel methods with significant imaging and non-imaging components, addressing practical applications and new datasets

Medical Image Analysis

Alejandro Frangi,Jerry Prince,Milan Sonka
Medical Image Analysis Book PDF

Author : Alejandro Frangi,Jerry Prince,Milan Sonka
Publisher : Academic Press
Page : 700 pages
File Size : 49,9 Mb
Release : 2023-09-20
Category : Technology & Engineering
ISBN : 9780128136584

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Medical Image Analysis by Alejandro Frangi,Jerry Prince,Milan Sonka Pdf

Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. Provides an authoritative description of key concepts and methods Includes tutorial-based sections that clearly explain principles and their application to different medical domains Presents a representative selection of topics to match a modern and relevant approach to medical image computing

Advances in Deep Learning for Medical Image Analysis

Archana Mire,Vinayak Elangovan,Shailaja Patil
Advances in Deep Learning for Medical Image Analysis Book PDF

Author : Archana Mire,Vinayak Elangovan,Shailaja Patil
Publisher : CRC Press
Page : 168 pages
File Size : 54,7 Mb
Release : 2022-04-28
Category : Technology & Engineering
ISBN : 9781000575958

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Advances in Deep Learning for Medical Image Analysis by Archana Mire,Vinayak Elangovan,Shailaja Patil Pdf

This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer’s disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

Machine Learning in Medical Imaging

Heung-Il Suk,Mingxia Liu,Pingkun Yan,Chunfeng Lian
Machine Learning in Medical Imaging Book PDF

Author : Heung-Il Suk,Mingxia Liu,Pingkun Yan,Chunfeng Lian
Publisher : Springer Nature
Page : 695 pages
File Size : 48,9 Mb
Release : 2019-10-09
Category : Computers
ISBN : 9783030326920

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Machine Learning in Medical Imaging by Heung-Il Suk,Mingxia Liu,Pingkun Yan,Chunfeng Lian Pdf

This book constitutes the proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 78 papers presented in this volume were carefully reviewed and selected from 158 submissions. They focus on major trends and challenges in the area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.