Artificial Intelligence and Machine Learning for Digital Pathology

Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance.

Artificial Intelligence and Machine Learning for Digital Pathology

Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ''fit-for-purpose samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.

Artificial Intelligence and Deep Learning in Pathology

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 ...

Artificial Intelligence and Deep Learning in Pathology

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 Deep Learning in Pathology

In Artificial Intelligence and Deep Learning in Pathology, with a team of experts, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to ...

Artificial Intelligence and Deep Learning in Pathology

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 Applications in Human Pathology

With chapters provided by true international experts in the field, this book gives real examples of the implementation of AI and machine learning in human pathology.Advances in machine learning and AI in general have propelled computational ...

Artificial Intelligence Applications in Human Pathology

Artificial Intelligence Applications in Human Pathology deals with the latest topics in biomedical research and clinical cancer diagnostics. With chapters provided by true international experts in the field, this book gives real examples of the implementation of AI and machine learning in human pathology.Advances in machine learning and AI in general have propelled computational and general pathology research. Today, computer systems approach the diagnostic levels achieved by humans for certain well-defined tasks in pathology. At the same time, pathologists are faced with an increased workload both quantitatively (numbers of cases) and qualitatively (the amount of work per case, with increasing treatment options and the type of data delivered by pathologists also expected to become more fine-grained). AI will support and leverage mathematical tools and implement data-driven methods as a center for data interpretation in modern tissue diagnosis and pathology. Digital or computational pathology will also foster the training of future computational pathologists, those with both pathology and non-pathology backgrounds, who will eventually decide that AI-based pathology will serve as an indispensable hub for data-related research in a global health care system.Some of the specific topics explored within include an introduction to DL as applied to Pathology, Standardized Tissue Sampling for Automated Analysis, integrating Computational Pathology into Histopathology workflows. Readers will also find examples of specific techniques applied to specific diseases that will aid their research and treatments including but not limited to; Tissue Cartography for Colorectal Cancer, Ki-67 Measurements in Breast Cancer, and Light-Sheet Microscopy as applied to Virtual Histology.The key role for pathologists in tissue diagnostics will prevail and even expand through interdisciplinary work and the intuitive use of an advanced and interoperating (AI-supported) pathology workflow delivering novel and complex features that will serve the understanding of individual diseases and of course the patient.

Artificial Intelligence in Medicine

In addition, it discusses open challenges and potential directions of deep learning-based pathological image computing and presents barriers to clinical adoption of AI in digital pathology. Keywords: Artificial intelligence; deep ...

Artificial Intelligence in Medicine

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

Intelligence Based Medicine

Digital pathology and artificial intelligence. Lancet Oncol 2019;20(5): e253À61. [152] Gruson D, Helleputte T, Rousseau P, et al. Data science, artificial intelligence, and machine learning: opportunities for laboratory medicine and the ...

Intelligence Based Medicine

Intelligence-Based Medicine: Data Science, Artificial Intelligence, and Human Cognition in Clinical Medicine and Healthcare provides a multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies with real life applications in healthcare and medicine. Authored by a senior physician-data scientist, the book presents an intellectual and academic interface between the medical and the data science domains that is symmetric and balanced. The content consists of basic concepts of artificial intelligence and its real-life applications in a myriad of medical areas as well as medical and surgical subspecialties. It brings section summaries to emphasize key concepts delineated in each section; mini-topics authored by world-renowned experts in the respective key areas for their personal perspective; and a compendium of practical resources, such as glossary, references, best articles, and top companies. The goal of the book is to inspire clinicians to embrace the artificial intelligence methodologies as well as to educate data scientists about the medical ecosystem, in order to create a transformational paradigm for healthcare and medicine by using this emerging new technology. Covers a wide range of relevant topics from cloud computing, intelligent agents, to deep reinforcement learning and internet of everything Presents the concepts of artificial intelligence and its applications in an easy-to-understand format accessible to clinicians and data scientists Discusses how artificial intelligence can be utilized in a myriad of subspecialties and imagined of the future Delineates the necessary elements for successful implementation of artificial intelligence in medicine and healthcare

The Era of Artificial Intelligence Machine Learning and Data Science in the Pharmaceutical Industry

In: Medical imaging 2019: digital pathology. International Society for Optics and Photonics; 2019. https://doi.org/10.1117/12.2512991. 55. Campanella G, et al. Clinical-grade computational pathology using weakly supervised deep learning ...

The Era of Artificial Intelligence  Machine Learning  and Data Science in the Pharmaceutical Industry

The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment where producing a single approved drug costs millions and takes many years of rigorous testing prior to its approval, reducing costs and time is of high interest. This book follows the journey that a drug company takes when producing a therapeutic, from the very beginning to ultimately benefitting a patient’s life. This comprehensive resource will be useful to those working in the pharmaceutical industry, but will also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics. Demonstrates how the prediction of toxic effects is performed, how to reduce costs in testing compounds, and its use in animal research Written by the industrial teams who are conducting the work, showcasing how the technology has improved and where it should be further improved Targets materials for a better understanding of techniques from different disciplines, thus creating a complete guide

ECML PKDD 2020 Workshops

Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, ... Artificial Intelligence and Machine Learning for Digital Pathology.

ECML PKDD 2020 Workshops

This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second International Workshop on eXplainable Knowledge Discovery in Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020. The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attribution 4.0 International License.

Current and Future Application of Artificial Intelligence in Clinical Medicine

[http://dx.doi.org/10.1109/TCYB.2019.2935141] [PMID: 31484154] [65] Parwani AV. Digital pathology as a platform for primary diagnosis and augmentation via deep learning. Artificial Intelligence and Deep Learning in Pathology.

Current and Future Application of Artificial Intelligence in Clinical Medicine

Current and Future Application of Artificial Intelligence in Clinical Medicine presents updates on the application of machine learning and deep learning techniques in medical procedures. . Chapters in the volume have been written by outstanding contributors from cancer and computer science institutes with the goal of providing updated knowledge to the reader. Topics covered in the book include 1) Artificial Intelligence (AI) applications in cancer diagnosis and therapy, 2) Updates in AI applications in the medical industry, 3) the use of AI in studying the COVID-19 pandemic in China, 4) AI applications in clinical oncology (including AI-based mining for pulmonary nodules and the use of AI in understanding specific carcinomas), 5) AI in medical imaging. Each chapter presents information on related sub topics in a reader friendly format. The combination of expert knowledge and multidisciplinary approaches highlighted in the book make it a valuable source of information for physicians and clinical researchers active in the field of cancer diagnosis and treatment (oncologists, oncologic surgeons, radiation oncologists, nuclear medicine physicians, and radiologists) and computer science scholars seeking to understand medical applications of artificial intelligence.

Pathology of Genetically Engineered and Other Mutant Mice

Society of toxicologic pathology digital pathology and image analysis special interest group article*: opinion on the application of artificial intelligence and machine learning to digital toxicologic pathology. Toxicol. Pathol.

Pathology of Genetically Engineered and Other Mutant Mice

An updated and comprehensive reference to pathology in every organ system in genetically modified mice The newly revised and thoroughly updated Second Edition of Pathology of Genetically Engineered and Other Mutant Mice delivers a comprehensive resource for pathologists and biomedical scientists tasked with identifying and understanding pathologic changes in genetically modified mice. The book is organized by body system, includes descriptions and explanations of a wide range of findings, as well as hundreds of color photographs illustrating both common and rare lesions that may be found in genetically engineered and wild type mice. The book is written by experienced veterinary and medical pathologists working in veterinary medical colleges, medical colleges, and research institutes. Covering the latest discoveries in mouse pathology resulting from advancements in biotechnology research over the last 30 years, this singular and accessible resource is a must-read for veterinary and medical pathologists and researchers working with genetically engineered and other mice. Readers will also benefit from: A thorough introduction to mouse pathology and mouse genetic nomenclature, as well as databases useful for analysis of mutant mice An exploration of concepts related to validating animal models, including the Cinderella Effect Practical discussions of basic necropsy methods and grading lesions for computational analyses Concise diagnostic approaches to the respiratory tract, the oral cavity and GI tract, the cardiovascular system, the liver and pancreas, the skeletal system, and other tissues As a one-stop and up to date reference on mouse pathology, Pathology of Genetically Engineered and Other Mutant Mice is an essential book for veterinary and medical pathologists, as well as for scientists, researchers, and toxicologists whose work brings them into contact with genetically modified mice.

Humanity Driven AI

In: Artificial Intelligence and Machine Learning for Digital Pathology, pp. 204–227. Springer (2020) 27. U.S. Food and Drug Administration: Proposed regulatory framework for modifications to artificial intelligence/machine learning ...

Humanity Driven AI

Artificial Intelligence (AI) is changing the world around us, and it is changing the way people are living, working, and entertaining. As a result, demands for understanding how AI functions to achieve and enhance human goals from basic needs to high level well-being (whilst maintaining human health) are increasing. This edited book systematically investigates how AI facilitates enhancing human needs in the digital age, and reports on the state-of-the-art advances in theories, techniques, and applications of humanity driven AI. Consisting of five parts, it covers the fundamentals of AI and humanity, AI for productivity, AI for well-being, AI for sustainability, and human-AI partnership. Humanity Driven AI creates an important opportunity to not only promote AI techniques from a humanity perspective, but also to invent novel AI applications to benefit humanity. It aims to serve as the dedicated source for the theories, methodologies, and applications on humanity driven AI, establishing state-of-the-art research, and providing a ground-breaking book for graduate students, research professionals, and AI practitioners.

Whole Slide Imaging

Currently, one of the hottest topics in digital pathology is the promise/potential of machine learning/artificial intelligence [64]. This field is still relatively new in digital pathology, and as such there are still some things that ...

Whole Slide Imaging

This book provides up-to-date and practical knowledge in all aspects of whole slide imaging (WSI) by experts in the field. This includes a historical perspective on the evolution of this technology, technical aspects of making a great whole slide image, the various applications of whole slide imaging and future applications using WSI for computer-aided diagnosis The goal is to provide practical knowledge and address knowledge gaps in this emerging field. This book is unique because it addresses an emerging area in pathology for which currently there is only limited information about the practical aspects of deploying this technology. For example, there are no established selection criteria for choosing new scanners and a knowledge base with the key information. The authors of the various chapters have years of real-world experience in selecting and implementing WSI solutions in various aspects of pathology practice. This text also discusses practical tips and pearls to address the selection of a WSI vendor, technology details, implementing this technology and provide an overview of its everyday uses in all areas of pathology. Chapters include important information on how to integrate digital slides with laboratory information system and how to streamline the “digital workflow” with the intent of saving time, saving money, reducing errors, improving efficiency and accuracy, and ultimately benefiting patient outcomes. Whole Slide Imaging: Current Applications and Future Directions is designed to present a comprehensive and state-of the-art approach to WSI within the broad area of digital pathology. It aims to give the readers a look at WSI with a deeper lens and also envision the future of pathology imaging as it pertains to WSI and associated digital innovations.

Advances in Artificial Intelligence Computation and Data Science

This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science.

Advances in Artificial Intelligence  Computation  and Data Science

Artificial intelligence (AI) has become pervasive in most areas of research and applications. While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity—in both time and memory requirements—for machine learning in large datasets. Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed for society. This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science. The benefits to the reader are manifold as researchers from similar or different fields can be aware of advanced developments and novel applications that can be useful for either immediate implementations or future scientific pursuit. Features: Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and biochemistry Provides recent developments in three evolving key areas and their complementary combinations: AI, computation, and data science Reports on applications in medicine and physiology, including cancer, neuroscience, and digital pathology Examines applications in life science, including systems biology, biochemistry, and even food technology This unique book, representing research from a team of international contributors, has not only real utility in academia for those in the medical and life sciences communities, but also a much wider readership from industry, science, and other areas of technology and education.

Artificial Intelligence for Human Computer Interaction A Modern Approach

Ertosun MG, Rubin DL (2015) Automated grading of gliomas using deep learning in digital pathology images: a modular approach with ensemble of convolutional neural networks. In: AMIA annual symposium proceedings, vol 2015.

Artificial Intelligence for Human Computer Interaction  A Modern Approach

This edited book explores the many interesting questions that lie at the intersection between AI and HCI. It covers a comprehensive set of perspectives, methods and projects that present the challenges and opportunities that modern AI methods bring to HCI researchers and practitioners. The chapters take a clear departure from traditional HCI methods and leverage data-driven and deep learning methods to tackle HCI problems that were previously challenging or impossible to address. It starts with addressing classic HCI topics, including human behaviour modeling and input, and then dedicates a section to data and tools, two technical pillars of modern AI methods. These chapters exemplify how state-of-the-art deep learning methods infuse new directions and allow researchers to tackle long standing and newly emerging HCI problems alike. Artificial Intelligence for Human Computer Interaction: A Modern Approach concludes with a section on Specific Domains which covers a set of emerging HCI areas where modern AI methods start to show real impact, such as personalized medical, design, and UI automation.

Artificial Intelligence and Machine Learning in Business Management

6.12 CANCER SCREENING AND MACHINE LEARNING AI is provided with a tremendous scope for interventions in cancer ... It can be termed as “Digital Pathology”. ... 106 Artificial Intelligence and Machine Learning in Business Management.

Artificial Intelligence and Machine Learning in Business Management

Artificial Intelligence and Machine Learning in Business Management The focus of this book is to introduce artificial intelligence (AI) and machine learning (ML) technologies into the context of business management. The book gives insights into the implementation and impact of AI and ML to business leaders, managers, technology developers, and implementers. With the maturing use of AI or ML in the field of business intelligence, this book examines several projects with innovative uses of AI beyond data organization and access. It follows the Predictive Modeling Toolkit for providing new insight on how to use improved AI tools in the field of business. It explores cultural heritage values and risk assessments for mitigation and conservation and discusses on-shore and off-shore technological capabilities with spatial tools for addressing marketing and retail strategies, and insurance and healthcare systems. Taking a multidisciplinary approach for using AI, this book provides a single comprehensive reference resource for undergraduate, graduate, business professionals, and related disciplines.

Travels on conferences

The session 'artificial intelligence from a pathologist's perspective' was chaired by Jorma Isola and Arvydas ... discussion followed and made again clear, that deep learning algorithms are now entering the world of digital pathology.

Travels on conferences

Once upon the time, in the 1960th a hen or an egg was delivered by the NASA (National Air and Space Agency) that dealt with all kinds of electronic communication to improve the astronauts' safety. It took another 25 years until the first international (European) Conference on Telepathology (electronic communication in pathology) took place in the philosophers' romantic city of Heidelberg in 1992. This book describes all conferences which occurred in a two years period, and the development of telepathology and digital pathology from 1992 until 2018. These fourteen conferences are a mirror of the technological progress in man's understanding and application of virtual worlds and digital medicine, starting from simple face to face communication to open access platforms or social and medical networks and automated image analysis and diagnosis systems. Conferences are information exchange occasions, and, in addition, background of funny events. The reader of this book can experience and enjoy both reliable and founded information of development and perspectives of digital pathology as well as mood and temper of participants who can be considered pioneers and experts in this specific scientific field.

Myeloproliferative Neoplasms An Issue of Hematology Oncology Clinics of North America

Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine 2018;27:317–28. 77. Rashidi HH, Tran NK, Betts EV, et al. Artificial intelligence and machine learning in pathology: the ...

Myeloproliferative Neoplasms  An Issue of Hematology Oncology Clinics of North America

This issue of Hematology/Oncology Clinics, guest edited by Drs. Ronald Hoffman, Ross Levine, John Mascarenhas, and Raajit Rampal, is dedicated to Myeloproliferative Neoplasms. This issue is one of six selected each year by the series consulting editors, Drs. George P. Canellos and Edward J. Benz. Topics in this issue include—but are not limited to— Overview of pathophysiology and potential drug targets, The role of the megakaryocyte, Epigenetics, Genetics, Novel technologies for understanding MPN biology, Important pathology considerations, Current Clinical investigations, Quality of life, Application of stem cell therapy, Immunotherapy approaches, Clinical unmet needs in ET/PV, Accelerated and blast phase MPNs, Epidemiology, Thrombotic and hemorrhagic complications, Murine modeling, The microenvironment in MPNs, MDS/MPN overlap syndrome, and Advancing effective clinical trial designs.

Advancement of Machine Intelligence in Interactive Medical Image Analysis

The use of digital imaging has also opened a new technological dimension for pathology. The images can be processed with Artificial intelligence and machine learning algorithms in computer-aided diagnosis systems or CAD system.

Advancement of Machine Intelligence in Interactive Medical Image Analysis

The book discusses major technical advances and research findings in the field of machine intelligence in medical image analysis. It examines the latest technologies and that have been implemented in clinical practice, such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy. This book provides insights into the basic science involved in processing, analysing, and utilising all aspects of advanced computational intelligence in medical decision-making based on medical imaging.

Pattern Recognition ICPR International Workshops and Challenges

Digital Pathology carries out multiple advantages, including ease of slides and cases sharing, organization of archived ... In particular, Machine Learning and other Artificial Intelligence derived methods raised for image feature ...

Pattern Recognition  ICPR International Workshops and Challenges

This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020.