Digital Pathology (eBook)

Digital Pathology: Implementation in Clinical Practice with AI Applications covers digital pathology applications in a clinical pathology laboratory setting, providing guidance and various tools for practical implementation. This comprehensive and detailed description of nuts and bolts of digital pathology implementation is unique and comes from the experience of high throughput scanning from a large surgical pathology laboratory. This is a valuable reference to prepare the end users not only about thoughtful launch of digital pathology but also to prepare for the future use of AI mediated decision support tools as the field advances. This book covers the entire range of this topic and can be used as a practical reference for individuals that wish to be part of the evolution of diagnostic pathology. Sections include pre-analytic, analytic, and post-analytic processes, encompassing hardware, software, space, staffing, quality control, laboratory information system applications, clinical validation, use cases and future applications, including computer assisted diagnosis and decision support tools. - Provides detailed aspects of the implementation of digital pathology in the clinical laboratory, including machine learning/artificial intelligence tools - Presents examples of how digital imaging and AI are drivers in research and development in radiology, with parallels to situations in pathology - Includes best practices and guidelines that can be practically applied by pathologists and data scientists

Dr. Hameed is the Chief of Surgical Pathology overseeing a large surgical pathology laboratory with over 80,000 cases and generating over a million slides per year. In the last several years her division has undertaken the task of digitizing slides (retrospective and prospective) using multiple platforms with a vision is to incorporate the use of digital information and digital information systems into all aspects of Pathology. She is also the Co-Director for the Warren Alpert Center for Digital and Computational Pathology at her institution. Through the Center an infrastructure has been established for deep learning which includes a high-performance computing cluster with a solid foundation to foster collaborations among experimental and diagnostic pathologists, computer and imaging scientists and experts in deep learning and artificial intelligence.

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Digital Pathology: Implementation in Clinical Practice with AI Applications covers digital pathology applications in a clinical pathology laboratory setting, providing guidance and various tools for practical implementation. This comprehensive and detailed description of nuts and bolts of digital pathology implementation is unique and comes from the experience of high throughput scanning from a large surgical pathology laboratory. This is a valuable reference to prepare the end users not only about thoughtful launch of digital pathology but also to prepare for the future use of AI mediated decision support tools as the field advances. This book covers the entire range of this topic and can be used as a practical reference for individuals that wish to be part of the evolution of diagnostic pathology. Sections include pre-analytic, analytic, and post-analytic processes, encompassing hardware, software, space, staffing, quality control, laboratory information system applications, clinical validation, use cases and future applications, including computer assisted diagnosis and decision support tools. - Provides detailed aspects of the implementation of digital pathology in the clinical laboratory, including machine learning/artificial intelligence tools- Presents examples of how digital imaging and AI are drivers in research and development in radiology, with parallels to situations in pathology- Includes best practices and guidelines that can be practically applied by pathologists and data scientists