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Medical Image Recognition Segmentation And Parsing

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Medical Image Recognition Segmentation and Parsing

Medical Image Recognition  Segmentation and Parsing Book
Author : S. Kevin Zhou
Publisher : Academic Press
Release : 2015-12-11
ISBN : 0128026766
Language : En, Es, Fr & De

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Book Description :

This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects Methods and theories for medical image recognition, segmentation and parsing of multiple objects Efficient and effective machine learning solutions based on big datasets Selected applications of medical image parsing using proven algorithms Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets Includes algorithms for recognizing and parsing of known anatomies for practical applications

Handbook of Medical Image Computing and Computer Assisted Intervention

Handbook of Medical Image Computing and Computer Assisted Intervention Book
Author : S. Kevin Zhou,Daniel Rueckert,Gabor Fichtinger
Publisher : Academic Press
Release : 2019-10-18
ISBN : 0128165863
Language : En, Es, Fr & De

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Book Description :

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. Presents the key research challenges in medical image computing and computer-assisted intervention Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Contains state-of-the-art technical approaches to key challenges Demonstrates proven algorithms for a whole range of essential medical imaging applications Includes source codes for use in a plug-and-play manner Embraces future directions in the fields of medical image computing and computer-assisted intervention

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging Book
Author : Qian Wang,Yinghuan Shi,Heung-Il Suk,Kenji Suzuki
Publisher : Springer
Release : 2017-09-06
ISBN : 3319673890
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works aimed to identify new cutting-edge techniques and their use in medical imaging.

Finite Element Method and Medical Imaging Techniques in Bone Biomechanics

Finite Element Method and Medical Imaging Techniques in Bone Biomechanics Book
Author : Rabeb Ben Kahla,Abdelwahed Barkaoui,Tarek Merzouki
Publisher : John Wiley & Sons
Release : 2020-01-02
ISBN : 1786305186
Language : En, Es, Fr & De

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Book Description :

Digital models based on data from medical images have recently become widespread in the field of biomechanics. This book summarizes medical imaging techniques and processing procedures, both of which are necessary for creating bone models with finite element methods. Chapter 1 introduces the main principles and the application of the most commonly used medical imaging techniques. Chapter 2 describes the major methods and steps of medical image analysis and processing. Chapter 3 presents a brief review of recent studies on reconstructed finite element bone models, based on medical images. Finally, Chapter 4 reveals the digital results obtained for the main bone sites that have been targeted by finite element modeling in recent years.

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Deep Learning and Convolutional Neural Networks for Medical Image Computing Book
Author : Le Lu,Yefeng Zheng,Gustavo Carneiro,Lin Yang
Publisher : Springer
Release : 2017-07-12
ISBN : 331942999X
Language : En, Es, Fr & De

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Book Description :

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

Hybrid Soft Computing for Image Segmentation

Hybrid Soft Computing for Image Segmentation Book
Author : Siddhartha Bhattacharyya,Paramartha Dutta,Sourav De,Goran Klepac
Publisher : Springer
Release : 2016-11-12
ISBN : 3319472232
Language : En, Es, Fr & De

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Book Description :

This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization. The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.

Medical Image Computing and Computer Assisted Intervention MICCAI 2020

Medical Image Computing and Computer Assisted Intervention   MICCAI 2020 Book
Author : Anne L. Martel,Purang Abolmaesumi,Danail Stoyanov,Diana Mateus,Maria A. Zuluaga,S. Kevin Zhou,Daniel Racoceanu,Leo Joskowicz
Publisher : Springer Nature
Release : 2020
ISBN : 303059713X
Language : En, Es, Fr & De

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Book Description :

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.

Medical Computer Vision

Medical Computer Vision Book
Author : Bjoern Menze,Georg Langs,Zhuowen Tu,Antonio Criminisi
Publisher : Springer
Release : 2011-02-02
ISBN : 3642184219
Language : En, Es, Fr & De

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Book Description :

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2010, held in Beijing, China, in September 2010 as a satellite event of the 13th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2010. The 10 revised full papers and 11 revised poster papers presented were carefully reviewed and selected from 38 initial submissions. The papers explore the use of modern image recognition technology in tasks such as semantic anatomy parsing, automatic segmentation and quantification, anomaly detection and categorization, data harvesting, semantic navigation and visualization, data organization and clustering, and general-purpose automatic understanding of medical images.

Riemannian Geometric Statistics in Medical Image Analysis

Riemannian Geometric Statistics in Medical Image Analysis Book
Author : Xavier Pennec,Stefan Sommer,Tom Fletcher
Publisher : Academic Press
Release : 2019-09
ISBN : 0128147253
Language : En, Es, Fr & De

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Book Description :

Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Content includes: - The foundations of Riemannian geometric methods for statistics on manifolds with emphasis on concepts rather than on proofs - Applications of statistics on manifolds and shape spaces in medical image computing - Diffeomorphic deformations and their applications As the methods described apply to domains such as signal processing (radar signal processing and brain computer interaction), computer vision (object and face recognition), and other domains where statistics of geometric features appear, this book is suitable for researchers and graduate students in medical imaging, engineering and computer science. - A complete reference covering both the foundations and state-of-the-art methods - Edited and authored by leading researchers in the field - Contains theory, examples, applications, and algorithms - Gives an overview of current research challenges and future applications

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis Book
Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publisher : Academic Press
Release : 2017-01-18
ISBN : 0128104090
Language : En, Es, Fr & De

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Book Description :

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. Covers common research problems in medical image analysis and their challenges Describes 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 Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache

Computational Retinal Image Analysis

Computational Retinal Image Analysis Book
Author : Emanuele Trucco,Tom MacGillivray,Yanwu Xu
Publisher : Academic Press
Release : 2019-06-29
ISBN : 0081028164
Language : En, Es, Fr & De

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Book Description :

Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more. Provides a unique, well-structured and integrated overview of retinal image analysis Gives insights into future areas, such as large-scale screening programs, precision medicine, and computer-assisted eye care Includes plans and aspirations of companies and professional bodies

Biomedical Texture Analysis

Biomedical Texture Analysis Book
Author : Adrien Depeursinge,Omar S Al-Kadi,J.Ross Mitchell
Publisher : Academic Press
Release : 2017-08-25
ISBN : 0128123214
Language : En, Es, Fr & De

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Book Description :

Biomedical Texture Analysis: Fundamentals, Applications, Tools and Challenges describes the fundamentals and applications of biomedical texture analysis (BTA) for precision medicine. It defines what biomedical textures (BTs) are and why they require specific image analysis design approaches when compared to more classical computer vision applications. The fundamental properties of BTs are given to highlight key aspects of texture operator design, providing a foundation for biomedical engineers to build the next generation of biomedical texture operators. Examples of novel texture operators are described and their ability to characterize BTs are demonstrated in a variety of applications in radiology and digital histopathology. Recent open-source software frameworks which enable the extraction, exploration and analysis of 2D and 3D texture-based imaging biomarkers are also presented. This book provides a thorough background on texture analysis for graduate students and biomedical engineers from both industry and academia who have basic image processing knowledge. Medical doctors and biologists with no background in image processing will also find available methods and software tools for analyzing textures in medical images. Defines biomedical texture precisely and describe how it is different from general texture information considered in computer vision Defines the general problem to translate 2D and 3D texture patterns from biomedical images to visually and biologically relevant measurements Describes, using intuitive concepts, how the most popular biomedical texture analysis approaches (e.g., gray-level matrices, fractals, wavelets, deep convolutional neural networks) work, what they have in common, and how they are different Identifies the strengths, weaknesses, and current challenges of existing methods including both handcrafted and learned representations, as well as deep learning. The goal is to establish foundations for building the next generation of biomedical texture operators Showcases applications where biomedical texture analysis has succeeded and failed Provides details on existing, freely available texture analysis software, helping experts in medicine or biology develop and test precise research hypothesis

Radiomics and Its Clinical Application

Radiomics and Its Clinical Application Book
Author : Jie Tian,Di Dong,Zhenyu Liu,Jingwei Wei
Publisher : Academic Press
Release : 2021-06-18
ISBN : 0128181028
Language : En, Es, Fr & De

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Book Description :

The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field. It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which make radiomics a powerful tool in the clinical setting. This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice. An introduction to the concepts of radiomics In-depth presentation of the core technologies and methods Summary of current radiomics research, perspective on the future of radiomics and the challenges ahead An introduction to several platforms that are planned to be built: cooperation, data sharing, software, and application platforms

Imaging Genetics

Imaging Genetics Book
Author : Adrian Dalca,Kayhan N. Batmanghelich,Mert Sabuncu,Li Shen
Publisher : Academic Press
Release : 2017-09-22
ISBN : 0128139692
Language : En, Es, Fr & De

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Book Description :

Imaging Genetics presents the latest research in imaging genetics methodology for discovering new associations between imaging and genetic variables, providing an overview of the state-of the-art in the field. Edited and written by leading researchers, this book is a beneficial reference for students and researchers, both new and experienced, in this growing area. The field of imaging genetics studies the relationships between DNA variation and measurements derived from anatomical or functional imaging data, often in the context of a disorder. While traditional genetic analyses rely on classical phenotypes like clinical symptoms, imaging genetics can offer richer insights into underlying, complex biological mechanisms. Contains an introduction describing how the field has evolved to the present, together with perspectives on its future direction and challenges Describes novel application domains and analytic methods that represent the state-of-the-art in the burgeoning field of imaging genetics Introduces a novel, large-scale analytic framework that involves multi-site, image-wide, genome-wide associations

Proceedings of the Future Technologies Conference FTC 2020 Volume 3

Proceedings of the Future Technologies Conference  FTC  2020  Volume 3 Book
Author : Kohei Arai
Publisher : Springer Nature
Release : 2021
ISBN : 3030630927
Language : En, Es, Fr & De

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Book Description :

Download Proceedings of the Future Technologies Conference FTC 2020 Volume 3 book written by Kohei Arai, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting

Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting Book
Author : Simone Balocco,Maria A. Zuluaga,Guillaume Zahnd,Su-Lin Lee,Stefanie Demirci
Publisher : Academic Press
Release : 2016-12-12
ISBN : 0128110198
Language : En, Es, Fr & De

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Book Description :

Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting presents imaging, treatment, and computed assisted technological techniques for diagnostic and intraoperative vascular imaging and stenting. These techniques offer increasingly useful information on vascular anatomy and function, and are poised to have a dramatic impact on the diagnosis, analysis, modeling, and treatment of vascular diseases. After setting out the technical and clinical challenges of vascular imaging and stenting, the book gives a concise overview of the basics before presenting state-of-the-art methods for solving these challenges. Readers will learn about the main challenges in endovascular procedures, along with new applications of intravascular imaging and the latest advances in computer assisted stenting. Brings together scientific researchers, medical experts, and industry partners working in different anatomical regions Presents an introduction to the clinical workflow and current challenges in endovascular Interventions Provides a review of the state-of-the-art methodologies in endovascular imaging and their applications Poses outstanding questions and discusses future research

Recent Advances in Information and Communication Technology 2017

Recent Advances in Information and Communication Technology 2017 Book
Author : Phayung Meesad,Sunantha Sodsee,Herwig Unger
Publisher : Springer
Release : 2017-06-17
ISBN : 3319606638
Language : En, Es, Fr & De

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Book Description :

This book includes selected contributions related to big data and data networking, presented at the 13th International Conference on Computing and Information Technology (IC2IT), which was held at the Arnoma Grand Hotel Bangkok, Thailand, July 6–7, 2017. The aim of the conference was to present emerging algorithms, methods and technologies with a high degree of originality, novelty and innovation addressing the conference theme `Mastering Data and Networking’. Section 1 and 2 discuss various aspects of data mining and corresponding applications. Section 3 focuses on speed and overhead networking optimisation problems, as well as energy problems of autonomous systems, which are becoming increasingly important. The key to addressing these problems is properly determining critical parameters. Section 4 sheds light on natural language processing, including extraction of trends and popularity and recognition of emotions as well as classic topics such as detection and classification.

Deep Learning for COVID Image Analysis

Deep Learning for COVID Image Analysis Book
Author : Hayit Greenspan,S. Kevin Zhou
Publisher : Academic Press
Release : 2021-10-01
ISBN : 0323902022
Language : En, Es, Fr & De

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Book Description :

Medical imaging is playing a role in the fight against COVID-19, in some countries as a key tool, from the screening and diagnosis through the entire treatment procedure. The extraordinarily rapid spread of this pandemic has demonstrated that a new disease entity with a subset of relatively unique characteristics can pose a major new clinical challenge that requires new diagnostic tools in imaging. The AI/Deep Learning Imaging community has shown in many recent publications that rapidly developed AI-based automated CT and Xray image analysis tools can achieve high accuracy in detection of Coronavirus positive patients as well as quantifying the disease burden. The typical developmental cycle and large number of studies required to develop AI algorithms for various disease entities is much too long to respond effectively to produce these software tools on demand. This suggests the strong need to develop software more rapidly, perhaps using transfer learning from existing algorithms, to train on a relatively limited number of cases, and to train on multiple datasets in various locations that may not be able to be easily combined due to privacy and security issues. Deep Learning for COVID Image Analysis provides a comprehensive overview of the most recently developed deep learning-based systems and solutions for COVID-19 image analysis, assembling a collection of state-of-the-art works for detection, severity analysis and predictive analysis, all of which are tools to support handling of the disease. Provides a comprehensive overview of research work on deep learning for COVID-19 image analysis Offers proven deep learning algorithms for medical image analysis applications Presents the research challenges in approaching a new disease

Artificial Intelligence and Machine Learning An Issue of Neuroimaging Clinics of North America E Book

Artificial Intelligence and Machine Learning   An Issue of Neuroimaging Clinics of North America  E Book Book
Author : Reza Forghani
Publisher : Elsevier Health Sciences
Release : 2020-10-23
ISBN : 0323712452
Language : En, Es, Fr & De

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Book Description :

This issue of Neuroimaging Clinics of North America focuses on Artificial Intelligence and Machine Learning and is edited by Dr. Reza Forghani. Articles will include: A Brief History of Artificial Intelligence; Evolution of Approaches for Computerized Image Analysis; Overview of Machine Learning Part 1: Classic Approaches; Overview of Machine Learning Part 2: Artificial Neural Networks & Deep Learning; Overview of Natural Language Processing; Artificial Intelligence & Stroke Imaging: An East Coast Perspective; Artificial Intelligence & Stroke Imaging: A West Coast Perspective; Artificial Intelligence Applications for Brain Tumor Imaging; Diverse Applications of Artificial Intelligence in Neuroradiology; Artificial Intelligence Applications for Head and Neck Imaging; Artificial Intelligence Applications for Predictive Analytics and Workflow Optimization; Artificial Intelligence, Advanced Visualization, and 3D Printing; Ethical & Legal Considerations for Artificial Intelligence; Comprehensive (or 360) Artificial Intelligence: Beyond Image Interpretation Alone, and more!

Computer Vision Approaches to Medical Image Analysis

Computer Vision Approaches to Medical Image Analysis Book
Author : Reinhard R. Beichel
Publisher : Springer Science & Business Media
Release : 2006-09-29
ISBN : 3540462570
Language : En, Es, Fr & De

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Book Description :

Medical imaging and medical image analysis are developing rapidly. While m- ical imaging has already become a standard of modern medical care, medical image analysis is still mostly performed visually and qualitatively. The ev- increasing volume of acquired data makes it impossible to utilize them in full. Equally important, the visual approaches to medical image analysis are known to su?er from a lack of reproducibility. A signi?cant researche?ort is devoted to developing algorithms for processing the wealth of data available and extracting the relevant information in a computerized and quantitative fashion. Medical imaging and image analysis are interdisciplinary areas combining electrical, computer, and biomedical engineering; computer science; mathem- ics; physics; statistics; biology; medicine; and other ?elds. Medical imaging and computer vision, interestingly enough, have developed and continue developing somewhat independently. Nevertheless, bringing them together promises to b- e?t both of these ?elds. This was the second time that a satellite workshop,solely devoted to medical image analysis issues, was held in conjunction with the European Conference on Computer Vision (ECCV), and we are optimistic that this will become a tradition at ECCV. We received 38 full-length paper submissions to the second Computer Vision Approaches to Medical Image Analysis (CVAMIA) Workshop, out of which 10 were accepted for oral and 11 for poster presentation after a rigorous peer-review process. In addition, the workshop included three invited talks. The ?rst was given by Maryellen Giger from the University of Chicago, USA — titled “Multi-Modality Breast CADx”.