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Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures

Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures PDF Author: Tanveer Syeda-Mahmood
Publisher: Springer Nature
ISBN: 3030609464
Category : Computers
Languages : en
Pages : 147

Book Description
This book constitutes the refereed joint proceedings of the 10th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2020, and the 9th International Workshop on Clinical Image-Based Procedures, CLIP 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. The 4 full papers presented at ML-CDS 2020 and the 9 full papers presented at CLIP 2020 were carefully reviewed and selected from numerous submissions to ML-CDS and 10 submissions to CLIP. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning. The CLIP workshops provides a forum for work centered on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.

Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures

Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures PDF Author: Tanveer Syeda-Mahmood
Publisher: Springer Nature
ISBN: 3030609464
Category : Computers
Languages : en
Pages : 147

Book Description
This book constitutes the refereed joint proceedings of the 10th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2020, and the 9th International Workshop on Clinical Image-Based Procedures, CLIP 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. The 4 full papers presented at ML-CDS 2020 and the 9 full papers presented at CLIP 2020 were carefully reviewed and selected from numerous submissions to ML-CDS and 10 submissions to CLIP. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning. The CLIP workshops provides a forum for work centered on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.

Breast Imaging

Breast Imaging PDF Author: Michael Fuchsjäger
Publisher: Springer Nature
ISBN: 3030949184
Category : Medical
Languages : en
Pages : 453

Book Description
This superbly illustrated book provides a thorough, up-to-date overview of diagnostic breast imaging and therapy. Drs. Elizabeth Morris, Michael Fuchsjäger, and Thomas Helbich, three experts in the field, have collaborated with colleagues from their institutions and selected medical centers to share their expertise. The coverage ranges from basic information on imaging technologies and interventional equipment and how to use them optimally to the application of advanced high-end techniques for screening and assessment in any given professional environment. Readers will find clear instruction on the various breast interventional procedures guided by stereotaxis, ultrasound, and magnetic resonance imaging in wide clinical use. The management of patients with ductal carcinoma in situ and high-risk breast cancer is considered separately. Furthermore, the role of minimally invasive therapy is examined, and advice is provided on post-therapy evaluation, including breast implants. A comprehensive diagnostic atlas with hundreds of images completes this volume and addresses the spectrum of various clinical situations.

Multimodal Learning for Clinical Decision Support

Multimodal Learning for Clinical Decision Support PDF Author: Tanveer Syeda-Mahmood
Publisher: Springer Nature
ISBN: 3030898474
Category : Computers
Languages : en
Pages : 125

Book Description
This book constitutes the refereed joint proceedings of the 11th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2021, held in conjunction with the 24th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2021, in Strasbourg, France, in October 2021. The workshop was held virtually due to the COVID-19 pandemic. The 10 full papers presented at ML-CDS 2021 were carefully reviewed and selected from numerous submissions. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.

Computer-Aided Oral and Maxillofacial Surgery

Computer-Aided Oral and Maxillofacial Surgery PDF Author: Jan Egger
Publisher: Academic Press
ISBN: 0128234237
Category : Computers
Languages : en
Pages : 284

Book Description
Computer-Aided Oral and Maxillofacial Surgery: Developments, Applications, and Future Perspectives is an ideal resource for biomedical engineers and computer scientists, clinicians and clinical researchers looking for an understanding on the latest technologies applied to oral and maxillofacial surgery. In facial surgery, computer-aided decisions supplement all kind of treatment stages, from a diagnosis to follow-up examinations. This book gives an in-depth overview of state-of-the-art technologies, such as deep learning, augmented reality, virtual reality and intraoperative navigation, as applied to oral and maxillofacial surgery. It covers applications of facial surgery that are at the interface between medicine and computer science. Examples include the automatic segmentation and registration of anatomical and pathological structures, like tumors in the facial area, intraoperative navigation in facial surgery and its recent developments and challenges for treatments like zygomatic implant placement. Provides comprehensive, state-of-the-art knowledge of interdisciplinary applications in facial surgery Presents recent algorithmic developments like Deep Learning, along with recent devices in augmented reality and virtual reality Includes clinical knowledge of two facials surgeons who give insights into the current clinical practice and challenges of facial surgeons in university hospitals in Austria and China

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support PDF Author: M. Jorge Cardoso
Publisher: Springer
ISBN: 3319675583
Category : Computers
Languages : en
Pages : 385

Book Description
This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 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 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Medical Content-Based Retrieval for Clinical Decision Support

Medical Content-Based Retrieval for Clinical Decision Support PDF Author: Henning Mueller
Publisher: Springer Science & Business Media
ISBN: 3642284590
Category : Business & Economics
Languages : en
Pages : 161

Book Description
This book constitutes the refereed proceedings of the Second MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS 2011, held in Toronto, Canada, in September 2011. The 11 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 17 submissions. The papers are divided on several topics on medical image retrieval with textual approaches, visual word based approaches, applications and multidimensional retrieval.

Medical Content-Based Retrieval for Clinical Decision Support

Medical Content-Based Retrieval for Clinical Decision Support PDF Author: Henning Müller
Publisher: Springer
ISBN: 3642117694
Category : Computers
Languages : en
Pages : 121

Book Description
This book constitutes the refereed proceedings of the first MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR_CBS 2009, held in London, UK, in September 2009. The 10 revised full papers were carefully reviewed and selected from numerous submissions. The papers are divide on several topics on medical image retrieval, clinical decision making and multimodal fusion.

Big Data in Multimodal Medical Imaging

Big Data in Multimodal Medical Imaging PDF Author: Ayman El-Baz
Publisher: CRC Press
ISBN: 1351380729
Category : Computers
Languages : en
Pages : 247

Book Description
There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support PDF Author: Danail Stoyanov
Publisher: Springer
ISBN: 3030008894
Category : Computers
Languages : en
Pages : 401

Book Description
This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 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 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging

Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging PDF Author: John S. H. Baxter
Publisher: Springer Nature
ISBN: 3031232232
Category : Computers
Languages : en
Pages : 144

Book Description
This book constitutes the refereed joint proceedings of the 1st International Workshop on Ethical & Philosophical Issues in Medical Imaging (EPIMI 2022); the 12th International Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support (ML-CDS 2022) and the 2nd International Workshop on Topological Data Analysis for Biomedical Imaging (TDA4BiomedicalImaging 2022), held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022, in Singapore, in September 2022. EPIMI includes five short papers about various humanistic aspects of medical image computing and computer-assisted interventions. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning. The TDA papers focus on Topological Data Analysis: a collection of techniques and tools that have matured from an increasing interest in the role topology plays in machine learning and data science.