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.

This volume provides a timely and useful introduction to the theory and practical application of image analysis in histology. This powerful research technique can be used to detect not only stored products in a cell (immunocytochemistry) but the synthetic machinery and the genes that control it (in situ hybridisation), as well as the specific binding sites that act as receptors for a molecule following its release (in vitro autoradiography). The book provides a good introduction for beginners before looking in greater detail at more advanced material in selected areas. The volume highlights the importance of technique in gathering quantitative information. The book is divided into four sections: introductory material, image acquisition, image processing, and applications. The applications areas include quantitative immunochemistry, quantification of nerves and neurotransmitters and automated grain counting in in situ hybridisation histochemistry.

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.

This book constitutes the refereed proceedings of the 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, held as a virtual event, in June 2021. The 28 full papers presented together with 30 short papers were selected from 138 submissions. The papers are grouped in topical sections on image analysis; predictive modelling; temporal data analysis; unsupervised learning; planning and decision support; deep learning; natural language processing; and knowledge representation and rule mining.

The second edition of this well-received book provides detailed information on the basic and advanced laboratory techniques in histopathology and cytology. It offers clear guidance on the principles and techniques of routine and special laboratory techniques. It also covers advanced laboratory techniques such as immunocytochemistry, flow cytometry, liquid-based cytology, polymerase chain reactions, tissue microarray, molecular technology, etc. The book's second edition covers several important recent topics with many new chapters, such as liquid biopsy, artificial neural network, digital pathology, and next-generation sequencing. Each chapter elucidates basic principle, practical methods, troubleshooting, and clinical applications of the technique. It includes multiple colored line drawings, microphotographs, and tables to illustrate each technique. The book is a helpful guide to the post-graduate students and fellows in pathology, practicing pathologists, as well as laboratory technicians, and research students.

This book constitutes the thoroughly refereed proceedings of the 15th International Conference on Image Analysis and Recognition, ICIAR 2018, held in Póvoa de Varzim, Portugal, in June 2018. The 91 full papers presented together with 15 short papers were carefully reviewed and selected from 179 submissions. The papers are organized in the following topical sections: Enhancement, Restoration and Reconstruction, Image Segmentation, Detection, Classication and Recognition, Indexing and Retrieval, Computer Vision, Activity Recognition, Traffic and Surveillance, Applications, Biomedical Image Analysis, Diagnosis and Screening of Ophthalmic Diseases, and Challenge on Breast Cancer Histology Images.

Thanks to the development and deployment of whole-slide imaging technology in pathology, glass slides previously observed under a traditional microscope are now scanned and converted to digital images, which are more beneficial for remote access, portability, and ease of sharing to facilitate telepathology. More importantly, digitization of glass slides paves the way towards the wide use of artificial intelligence (AI) tools including machine/deep learning algorithms, resulting in improved diagnostic accuracy. In the past decade, a large number of studies have demonstrated the remarkable success of AI, particularly deep learning, in digital pathology, such as tumor region identification, metastasis detection, and patient prognosis. Differing from handcrafted feature-based approaches that take advantage of domain knowledge to delineate specific morphological measurements (e.g., nuclei shape and size and tissue texture) in the images as features for training, deep learning is a paradigm of feature learning entirely driven by the image data and/or labels. Herein, the use of deep learning in pathological diagnosis can not only handle increased workloads and expertise shortages but also obviate subjective diagnosis from pathologists. Yet there remain many scientific and technological challenges associated with the efficiency of deep learning algorithms for use in clinical practice. For example, deep learning requires a sufficient amount of training data for generalization and suffers from a lack of feature interpretability. The overarching goal of this special issue is to highlight novel research accomplishments and directions, related to advanced AI methodology development and applications in digital pathology.