Almost 20 years have passed since the commercial introduction of whole-slide imaging (WSI) scanners. During this time, the creation of various WSI devices with the ability to digitize an entire glass slide has transformed the field of pathology. Parallel advances in computational technology and storage have permitted rapid processing of large-scale WSI datasets. This article provides an overview of important past and present efforts related to WSI. An account of how the virtual microscope evolved from the need to visualize and manage satellite data for earth science applications is provided. The article also discusses important milestones beginning from the first WSI scanner designed by Bacus to the Food and Drug Administration approval of the first digital pathology system for primary diagnosis in surgical pathology. As pathology laboratories commit to going fully digitalize, the need has emerged to include WSIs into an enterprise-level vendor-neutral archive (VNA). The different types of VNAs available are reviewed as well as how best to implement them and how pathology can benefit from participating in this effort. Differences between traditional image algorithms that extract pixel-, object-, and semantic-level features versus deep learning methods are highlighted. The need for large-scale data management, analysis, and visualization in computational pathology is also addressed.

In light of the recent success of artificial intelligence (AI) in computer vision applications, many researchers and physicians expect that AI would be able to assist in many tasks in digital pathology. Although opportunities are both manifest and tangible, there are clearly many challenges that need to be overcome in order to exploit the AI potentials in computational pathology. In this paper, we strive to provide a realistic account of all challenges and opportunities of adopting AI algorithms in digital pathology from both engineering and pathology perspectives.

Biomedical informatics is the science of information, where information is defined as data with meaning. This definition identifies a fundamental challenge for informaticians: connecting with the healthcare team by enabling the acquisition, retrieval, and processing of information within the cognitive capabilities of the human brain. Informaticians can become aware of the constraints involved with cognitive processing and with workplace factors that impact how information is acquired and used to facilitate an improved user interface providing information to healthcare teams. Constraints affecting persons in the work environment include as follows: (1) cognitive processing of information; (2) cognitive load and memory capacity; (3) stress-affecting cognition; (4) cognitive distraction, attention, and multitasking; (5) cognitive bias and flexibility; (6) communication barriers; and (7) workplace environment. The human brain has a finite cognitive load capacity for processing new information. Short-term memory has limited throughput for processing of new informational items, while long-term memory supplies immediate simultaneous access to multiple informational items. Visual long-term memories can be extensive and detailed. Attention may be task dependent and highly variable among persons and requires maintaining control over distracting information. Multitasking reduces the effectiveness of working memory applied to each task. Transfer of information from person to person, or machine to person, is subject to cognitive bias and environmental stressors. High-stress levels increase emotional arousal to reduce memory formation and retrieval. The workplace environment can impact cognitive processes and stress, so maintaining civility augments cognitive abilities. Examples of human-computer interfaces employing principles of cognitive informatics inform design of systems to enhance the user interface.

The advent of the digital pathology has introduced new avenues of diagnostic medicine. Among them, crowdsourcing has attracted researchers' attention in the recent years, allowing them to engage thousands of untrained individuals in research and diagnosis. While there exist several articles in this regard, prior works have not collectively documented them. We, therefore, aim to review the applications of crowdsourcing in human pathology in a semi-systematic manner. We first, introduce a novel method to do a systematic search of the literature. Utilizing this method, we, then, collect hundreds of articles and screen them against a predefined set of criteria. Furthermore, we crowdsource part of the screening process, to examine another potential application of crowdsourcing. Finally, we review the selected articles and characterize the prior uses of crowdsourcing in pathology.

Context: Tissue banking informatics deals with standardized annotation, collection and storage of biospecimens that can further be shared by researchers. Over the last decade, the Department of Biomedical Informatics (DBMI) at the University of Pittsburgh has developed various tissue banking informatics tools to expedite translational medicine research. In this review, we describe the technical approach and capabilities of these models. Design: Clinical annotation of biospecimens requires data retrieval from various clinical information systems and the de-identification of the data by an honest broker. Based upon these requirements, DBMI, with its collaborators, has developed both Oracle-based organ-specific data marts and a more generic, model-driven architecture for biorepositories. The organ-specific models are developed utilizing Oracle 9.2.0.1 server tools and software applications and the model-driven architecture is implemented in a J2EE framework. Result: The organ-specific biorepositories implemented by DBMI include the Cooperative Prostate Cancer Tissue Resource ( http://www.cpctr.info/ ), Pennsylvania Cancer Alliance Bioinformatics Consortium ( http://pcabc.upmc.edu/main.cfm ), EDRN Colorectal and Pancreatic Neoplasm Database ( http://edrn.nci.nih.gov/ ) and Specialized Programs of Research Excellence (SPORE) Head and Neck Neoplasm Database ( http://spores.nci.nih.gov/current/hn/index.htm ). The model-based architecture is represented by the National Mesothelioma Virtual Bank ( http://mesotissue.org/ ). These biorepositories provide thousands of well annotated biospecimens for the researchers that are searchable through query interfaces available via the Internet. Conclusion: These systems, developed and supported by our institute, serve to form a common platform for cancer research to accelerate progress in clinical and translational research. In addition, they provide a tangible infrastructure and resource for exposing research resources and biospecimen services in collaboration with the clinical anatomic pathology laboratory information system (APLIS) and the cancer registry information systems.