Deep Learning for Cancer Diagnosis
Deep Learning for Cancer Diagnosis

Author: Utku Kose

Publisher: Springer Nature

Published: 2020-09-12

Total Pages: 311

ISBN-13: 9811563217

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This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.

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

Imaging Flow Cytometry
Imaging Flow Cytometry

Author: Natasha S. Barteneva

Publisher: Humana

Published: 2015-11-23

Total Pages: 0

ISBN-13: 9781493933006

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This detailed volume for the first time explores techniques and protocols involving quantitative imaging flow cytometry (IFC), which has revolutionized our ability to analyze cells, cellular clusters, and populations in a remarkable fashion. Beginning with an introduction to technology, the book continues with sections addressing protocols for studies on the cell nucleus, nucleic acids, and FISH techniques using an IFC instrument, immune response analysis and drug screening, IFC protocols for apoptosis and cell death analysis, as well as morphological analysis and the identification of rare cells. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Imaging Flow Cytometry: Methods and Protocols will be a critical source for all laboratories seeking to implement IFC in their research studies.

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.

Dermoscopy Image Analysis
Dermoscopy Image Analysis

Author: M. Emre Celebi

Publisher: CRC Press

Published: 2015-10-16

Total Pages: 458

ISBN-13: 1482253275

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Dermoscopy is a noninvasive skin imaging technique that uses optical magnification and either liquid immersion or cross-polarized lighting to make subsurface structures more easily visible when compared to conventional clinical images. It allows for the identification of dozens of morphological features that are particularly important in identifyin

Current Applications of Deep Learning in Cancer Diagnostics
Current Applications of Deep Learning in Cancer Diagnostics

Author: Jyotismita Chaki

Publisher: CRC Press

Published: 2023-02-22

Total Pages: 189

ISBN-13: 1000836150

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This book examines deep learning-based approaches in the field of cancer diagnostics, as well as pre-processing techniques, which are essential to cancer diagnostics. Topics include introduction to current applications of deep learning in cancer diagnostics, pre-processing of cancer data using deep learning, review of deep learning techniques in oncology, overview of advanced deep learning techniques in cancer diagnostics, prediction of cancer susceptibility using deep learning techniques, prediction of cancer reoccurrence using deep learning techniques, deep learning techniques to predict the grading of human cancer, different human cancer detection using deep learning techniques, prediction of cancer survival using deep learning techniques, complexity in the use of deep learning in cancer diagnostics, and challenges and future scopes of deep learning techniques in oncology.

The Paris System for Reporting Urinary Cytology
The Paris System for Reporting Urinary Cytology

Author: Dorothy L. Rosenthal

Publisher: Springer

Published: 2015-12-16

Total Pages: 177

ISBN-13: 3319228641

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This book describes a novel and proven approach to cytologically classify urinary samples for the detection of bladder cancer and lesions of the upper urinary tract. The new method is based on the collective experience of knowledgeable cytopathologists who have tested the terminology within their own laboratories for reproducibility and predictability of neoplasms of the urinary tract. Accompanying the written criteria for each diagnostic category are meticulously photographed exemplars of the cellular features, with cogently annotated descriptions of the photographs. The book thereby performs as an atlas for microscopists involved in diagnostic cytopathology at all levels of their education. Included in the targeted readership are experienced pathologists, cytotechnologists, and students of both professional groups. The new terminology also considers the clinical aspects of patient management. Written by experts in the field who convened at the 18th International Congress of Cytology in Paris, The Paris System for Reporting Urinary Cytology presents a global standard for reporting and a new philosophic approach that maximizes the strengths of detecting the potentially lethal high grade lesions by urinary cytology, and recognizes without apology the inability to reliably detect the low grade lesions in urinary cytology. The Concept has been endorsed by the American Society Of Cytopathology, and the International Academy of Cytology.

Deep Learning Applications in Medical Imaging
Deep Learning Applications in Medical Imaging

Author: Saxena, Sanjay

Publisher: IGI Global

Published: 2020-10-16

Total Pages: 274

ISBN-13: 1799850722

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Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.

Deep Learning and Convolutional Neural Networks for Medical Image Computing
Deep Learning and Convolutional Neural Networks for Medical Image Computing

Author: Le Lu

Publisher: Springer

Published: 2017-07-12

Total Pages: 327

ISBN-13: 331942999X

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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.

Digital Pathology
Digital Pathology

Author: Constantino Carlos Reyes-Aldasoro

Publisher: Springer

Published: 2019-07-03

Total Pages: 200

ISBN-13: 3030239373

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This book constitutes the refereed proceedings of the 15th European Congress on Digital Pathology, ECDP 2019, held in Warwick, UK in April 2019. The 21 full papers presented in this volume were carefully reviewed and selected from 30 submissions. The congress theme will be Accelerating Clinical Deployment, with a focus on computational pathology and leveraging the power of big data and artificial intelligence to bridge the gaps between research, development, and clinical uptake.