Medical Image Learning with Limited and Noisy Data
Medical Image Learning with Limited and Noisy Data

Author: Zhiyun Xue

Publisher: Springer Nature

Published: 2023-11-08

Total Pages: 274

ISBN-13: 3031449177

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This book consists of full papers presented in the 2nd workshop of ”Medical Image Learning with Noisy and Limited Data (MILLanD)” held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). The 24 full papers presented were carefully reviewed and selected from 38 submissions. The conference focused on challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.

Medical Image Learning with Limited and Noisy Data
Medical Image Learning with Limited and Noisy Data

Author: Ghada Zamzmi

Publisher: Springer Nature

Published: 2022-09-21

Total Pages: 243

ISBN-13: 3031167600

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This book constitutes the proceedings of the First Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with MICCAI 2022. The conference was held in Singapore. For this workshop, 22 papers from 54 submissions were accepted for publication. They selected papers focus on the challenges and limitations of current deep learning methods applied to limited and noisy medical data and present new methods for training models using such imperfect data.

Deep Learning for Medical Image Analysis
Deep Learning for Medical Image Analysis

Author: S. Kevin Zhou

Publisher: Academic Press

Published: 2023-11-23

Total Pages: 544

ISBN-13: 0323858880

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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

Deep Learning in Medical Image Analysis
Deep Learning in Medical Image Analysis

Author: Gobert Lee

Publisher: Springer Nature

Published: 2020-02-06

Total Pages: 184

ISBN-13: 3030331288

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This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

Artificial Intelligence in Medical Imaging
Artificial Intelligence in Medical Imaging

Author: Erik R. Ranschaert

Publisher: Springer

Published: 2019-01-29

Total Pages: 369

ISBN-13: 3319948784

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This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

Deep Learning for Biomedical Image Reconstruction
Deep Learning for Biomedical Image Reconstruction

Author: Jong Chul Ye

Publisher: Cambridge University Press

Published: 2023-09-30

Total Pages: 365

ISBN-13: 1316517519

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Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications. The background theory of deep learning is introduced step-by-step, and by incorporating modeling fundamentals this book explains how to implement deep learning in a variety of modalities, including X-ray, CT, MRI and others. Real-world examples demonstrate an interdisciplinary approach to medical image reconstruction processes, featuring numerous imaging applications. Recent clinical studies and innovative research activity in generative models and mathematical theory will inspire the reader towards new frontiers. This book is ideal for graduate students in Electrical or Biomedical Engineering or Medical Physics.

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007

Author: Nicholas Ayache

Publisher: Springer

Published: 2007-10-12

Total Pages: 1020

ISBN-13: 3540757597

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This title is part of a two-volume set that constitute the refereed proceedings of the 10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007. Coverage in this second volume includes computer assisted intervention and robotics, visualization and interaction, neuroscience image computing, computational anatomy, innovative clinical and biological applications, general biological imaging computing, computational physiology.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Author: Anne L. Martel

Publisher: Springer Nature

Published: 2020-10-02

Total Pages: 849

ISBN-13: 3030597105

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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 applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

Artificial Intelligence
Artificial Intelligence

Author: Lu Fang

Publisher: Springer Nature

Published: 2022-12-16

Total Pages: 639

ISBN-13: 3031205030

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This three-volume set LNCS 13604-13606 constitutes revised selected papers presented at the Second CAAI International Conference on Artificial Intelligence, held in Beijing, China, in August 2022. CICAI is a summit forum in the field of artificial intelligence and the 2022 forum was hosted by Chinese Association for Artificial Intelligence (CAAI). The 164 papers were thoroughly reviewed and selected from 521 submissions. CICAI aims to establish a global platform for international academic exchange, promote advanced research in AI and its affiliated disciplines such as machine learning, computer vision, natural language, processing, and data mining, amongst others.