Download Artificial Intelligence and Deep Learning in Pathology Ebook PDF

Artificial Intelligence and Deep Learning in Pathology E-Book

Artificial Intelligence and Deep Learning in Pathology E-Book
A Book

by Stanley Cohen

  • Publisher : Elsevier Health Sciences
  • Release : 2020-06-02
  • Pages : 288
  • ISBN : 0323675379
  • Language : En, Es, Fr & De
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Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.

Artificial Intelligence and Machine Learning for Digital Pathology

Artificial Intelligence and Machine Learning for Digital Pathology
State-of-the-Art and Future Challenges

by Andreas Holzinger

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 3030504026
  • Language : En, Es, Fr & De
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Artificial Intelligence and Deep Learning in Pathology

Artificial Intelligence and Deep Learning in Pathology
A Book

by Stanley Cohen

  • Publisher : Elsevier
  • Release : 2020-06
  • Pages : 250
  • ISBN : 9780323675383
  • Language : En, Es, Fr & De
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Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.

Artificial Intelligence In Medicine

Artificial Intelligence In Medicine
A Book

by Peter Szolovits

  • Publisher : Routledge
  • Release : 2019-03-13
  • Pages : 226
  • ISBN : 0429728468
  • Language : En, Es, Fr & De
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This book introduces the field of artificial intelligence in medicine, a new research area that combines sophisticated representational and computing techniques with the insights of expert physicians to produce tools for improving health care. An introductory chapter describes the historical and technical foundations of the work and provides an overview of the current state of the art and research directions. The authors then describe four prototype computer programs that tackle difficult clinical problems in a manner similar to that of an expert physician. The programs presented are internist, a diagnostic aid that combines a large database of disease/manifestation associations with techniques for problem formulation; expert and the Glaucoma Program which use physiological models for the diagnosis and treatment of eye disease; mycin, a rule-based program for diagnosis and therapy selection for infectious diseases; and the Digitalis Therapy Advisor, which aids the physician in prescribing the right dose of the drug digitalis and also explains its actions.

Artificial Intelligence in Medical Imaging

Artificial Intelligence in Medical Imaging
Opportunities, Applications and Risks

by Erik R. Ranschaert,Sergey Morozov,Paul R. Algra

  • Publisher : Springer
  • Release : 2019-01-29
  • Pages : 373
  • ISBN : 3319948784
  • Language : En, Es, Fr & De
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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 Medicine

Deep Medicine
How Artificial Intelligence Can Make Healthcare Human Again

by Eric Topol

  • Publisher : Basic Books
  • Release : 2019-03-12
  • Pages : 400
  • ISBN : 1541644646
  • Language : En, Es, Fr & De
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One of America's top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard. Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved.

Digital Pathology

Digital Pathology
A Book

by Liron Pantanowitz,Anil V. Parwani

  • Publisher : Unknown Publisher
  • Release : 2017
  • Pages : 304
  • ISBN : 9780891896104
  • Language : En, Es, Fr & De
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The definitive, complete reference of digital pathology! An extraordinarily comprehensive and complete book for individuals with anything from minimal knowledge to deep, accomplished experience in digital pathology. Easy to read and plainly written, Digital Pathology examines the history and technological evolution of digital pathology, from the birth of scanning technology and telepathology to three-dimensional imaging on large multi-touch displays and computer aided diagnosis. A must-have book for anyone wishing to learn more about and work in this exciting and critical information environment including pathologists, laboratory professionals, students and any other medical practitioners with a particular interest in the history and future of digital pathology. It can also be a useful reference for anyone, medical or non-medical, who have an interest in learning more about the field. Digital pathology is truly a game changer, and this book is a crucial tool for anyone wishing to know more. Subjects discussed in depth include: Static digital imaging; basics and clinical use. Digital imaging processes. Telepathology. While slide imaging. Clinical applications of whole slide imaging. Digital pathology for educational, quality improvement, research and other settings. Forensic digital imaging.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine
17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings

by David Riaño,Szymon Wilk,Annette ten Teije

  • Publisher : Springer
  • Release : 2019-06-19
  • Pages : 429
  • ISBN : 303021642X
  • Language : En, Es, Fr & De
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This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

Medical Imaging

Medical Imaging
Artificial Intelligence, Image Recognition, and Machine Learning Techniques

by K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey

  • Publisher : CRC Press
  • Release : 2019-08-20
  • Pages : 238
  • ISBN : 0429642490
  • Language : En, Es, Fr & De
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The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.

Histopathological Image Analysis

Histopathological Image Analysis
A Book

by Gurcan

  • Publisher : Wiley-Blackwell
  • Release : 2021
  • Pages : 256
  • ISBN : 9781119099093
  • Language : En, Es, Fr & De
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Artificial Intelligence in Medicine

Artificial Intelligence in Medicine
Technical Basis and Clinical Applications

by Lei Xing,Maryellen L. Giger,James K Min

  • Publisher : Academic Press
  • Release : 2020-09-15
  • Pages : 570
  • ISBN : 0128212594
  • Language : En, Es, Fr & De
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Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI. The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticism. By understanding how AI works, and developing an appreciation for both limitations and strengths, clinicians can harness its computational power to streamline workflow and improve patient care. It also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches. On the other hand, computers scientists and data analysts can provide solutions, but often lack easy access to clinical insight that may help focus their efforts. This book provides vital background knowledge to help bring these two groups together, and to engage in more streamlined dialogue to yield productive collaborative solutions in the field of medicine. Provides history and overview of artificial intelligence, as narrated by pioneers in the field Discusses broad and deep background and updates on recent advances in both medicine and artificial intelligence that enabled the application of artificial intelligence Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach

Deep Learning for the Life Sciences

Deep Learning for the Life Sciences
Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More

by Bharath Ramsundar,Peter Eastman,Patrick Walters,Vijay Pande

  • Publisher : O'Reilly Media
  • Release : 2019-04-10
  • Pages : 238
  • ISBN : 1492039802
  • Language : En, Es, Fr & De
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Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working

Machine Learning for Health Informatics

Machine Learning for Health Informatics
State-of-the-Art and Future Challenges

by Andreas Holzinger

  • Publisher : Springer
  • Release : 2016-12-09
  • Pages : 481
  • ISBN : 3319504789
  • Language : En, Es, Fr & De
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Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.

Artificial Intelligence in Practice

Artificial Intelligence in Practice
How 50 Successful Companies Used AI and Machine Learning to Solve Problems

by Bernard Marr

  • Publisher : John Wiley & Sons
  • Release : 2019-04-15
  • Pages : 352
  • ISBN : 1119548985
  • Language : En, Es, Fr & De
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Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce.

Artificial Intelligence for Computational Modeling of the Heart

Artificial Intelligence for Computational Modeling of the Heart
A Book

by Tommaso Mansi,Tiziano Passerini,Dorin Comaniciu

  • Publisher : Academic Press
  • Release : 2019-11-25
  • Pages : 274
  • ISBN : 0128168951
  • Language : En, Es, Fr & De
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Artificial Intelligence for Computational Modeling of the Heart presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient’s heart by combining computational modeling of heart physiology and artificial intelligence. The book first introduces the major aspects of multi-scale modeling of the heart, along with the compromises needed to achieve subject-specific simulations. Reader will then learn how AI technologies can unlock robust estimations of cardiac anatomy, obtain meta-models for real-time biophysical computations, and estimate model parameters from routine clinical data. Concepts are all illustrated through concrete clinical applications. Presents recent advances in computational modeling of heart function and artificial intelligence technologies for subject-specific applications Discusses AI-based technologies for robust anatomical modeling from medical images, data-driven reduction of multi-scale cardiac models, and estimations of physiological parameters from clinical data Illustrates the technology through concrete clinical applications and discusses potential impacts and next steps needed for clinical translation

Digital Pathology

Digital Pathology
15th European Congress, ECDP 2019, Warwick, UK, April 10–13, 2019, Proceedings

by Constantino Carlos Reyes-Aldasoro,Andrew Janowczyk,Mitko Veta,Peter Bankhead,Korsuk Sirinukunwattana

  • Publisher : Springer
  • Release : 2019-07-03
  • Pages : 192
  • ISBN : 3030239373
  • Language : En, Es, Fr & De
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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.

Breast Cancer and Surgery

Breast Cancer and Surgery
A Book

by Nilufer Bulut

  • Publisher : BoD – Books on Demand
  • Release : 2018-11-21
  • Pages : 226
  • ISBN : 1789235669
  • Language : En, Es, Fr & De
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The book 'Breast Cancer and Surgery' summarizes the treatment options from the onset of breast carcinogenesis to early-local advanced and metastatic breast cancer. Chemotherapy alternatives, drug resistance and local and surgical treatment preferences are extensively discussed and this information is especially directed at clinicians, researchers, and students. This book includes a comparison between different chemotherapy agents and targeted therapies with published phase II-III studies. The importance of palliative care and dietary supplements administered during the treatment course in reducing the comorbidity of patients is emphasized. Photodynamic treatments have been included in this book. A comprehensive and up-to-date information exchange that can be accessed through a single source is provided to all researchers interested in breast cancer.

Machine Learning and Medical Imaging

Machine Learning and Medical Imaging
A Book

by Guorong Wu,Dinggang Shen,Mert Sabuncu

  • Publisher : Academic Press
  • Release : 2016-08-11
  • Pages : 512
  • ISBN : 0128041145
  • Language : En, Es, Fr & De
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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

Artificial Intelligence Applications and Innovations

Artificial Intelligence Applications and Innovations
16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part II

by Ilias Maglogiannis,Lazaros Iliadis,Elias Pimenidis

  • Publisher : Springer Nature
  • Release : 2020-05-29
  • Pages : 458
  • ISBN : 3030491862
  • Language : En, Es, Fr & De
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This 2 volume-set of IFIP AICT 583 and 584 constitutes the refereed proceedings of the 16th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2020, held in Neos Marmaras, Greece, in June 2020.* The 70 full papers and 5 short papers presented were carefully reviewed and selected from 149 submissions. They cover a broad range of topics related to technical, legal, and ethical aspects of artificial intelligence systems and their applications and are organized in the following sections: Part I: classification; clustering - unsupervised learning -analytics; image processing; learning algorithms; neural network modeling; object tracking - object detection systems; ontologies - AI; and sentiment analysis - recommender systems. Part II: AI ethics - law; AI constraints; deep learning - LSTM; fuzzy algebra - fuzzy systems; machine learning; medical - health systems; and natural language. *The conference was held virtually due to the COVID-19 pandemic.

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
A Book

by S. Kevin Zhou,Hayit Greenspan,Dinggang Shen

  • Publisher : Academic Press
  • Release : 2017-01-18
  • Pages : 458
  • ISBN : 0128104090
  • Language : En, Es, Fr & De
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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