Call for Paper:
Track 2 Digital pathology
SUB TRACK Digital Pathology, Whole-Slide Imaging, microscopy, Glass slides, diagnoses, diagnostic medicine, pathologist, skills, skills quantitative image analysis, machine learning, artificial intelligence, diagnoses for patients, scientist, primary diagnosis, tele-pathology, cellular structures, scanner, scanner technology, Radiology,
The field of digital pathology is a branch of pathology that focuses on data management using data derived from digitalized specimen slides. Digital pathology makes use of virtual microscopy through computer-based technology. Glass slides are transformed into digital slides that can be managed, shared, viewed, and analysed on a computer screen. The field of digital pathology is expanding and has applications in diagnostic medicine, with the aim of achieving effective and affordable diagnoses, prognosis, and prediction of diseases due to the success in Artificial Intelligence and Machine Learning. Whole-Slide Imaging (WSI), which is another name for virtual microscopy, is practised.
Although the concept of digital pathology has been widely adopted during the past ten years, it is not entirely new. The idea of digital pathology has been around for more than a century, when scientists first created specialised tools to transfer microscope images onto photographic plates so that they could be saved, used later, and shared with other researchers to help them understand pathology.
The increasing adoption of digital pathology has also been aided by major advancements in computational and storage technologies, along with equipment advancements.
Digital pathology has become firmly established in contemporary clinical practise as a result of the development of whole-slide imaging, the accelerated speed of networks, and the more affordable, more accessible storage options. This has significantly advanced our understanding of the nature of the disease.
Digital Pathology AI (Artificial Intelligence)
A pathology AI system is a piece of software that offers automated pathology or aids pathologists in their work. A pathology AI system’s main function is to use machine learning and image analysis to interpret digital slide images. A task, like as generating a diagnostic or a score, or a subtask, such as sorting cells into several cell kinds, can be learned from data using machine learning. We will concentrate our discussion on a few machine learning techniques, such as decision trees, random forests, and deep learning.
Deep learning has raised the profile of artificial intelligence in recent years (AI). In computer vision, where the feature detection could not be accomplished properly by writing image analysis algorithms, deep learning has surmounted significant obstacles. A deep learning network may mimic expert human performance by learning extremely complicated visual properties only from image input. Deep learning takes a large amount of data and computing power.
Digital Pathology Society Japanese Society of Toxicologic Pathology, Pathological Society, The Pathological Society of Great Britain & Ireland, United States and Canadian Academy of Pathology, American Society for Investigative Pathology New York Pathological Society, American Society for Cytotechnology, United States and Canadian Academy of Pathology
Digital Pathology Association Digital Pathology Association, American Association of Neuropathologists, American Board of Pathology, Pathology & Laboratory Medicine, Southeastern Pathology Associates Medical associations based in the United States, American College of Veterinary Pathologists, Pennsylvania Association of Pathologists, Associates In Pathology S.C,
Digital Pathology University University of Leeds, Brown University, Digital PathologyUniversity of Michigan, Digital Pathology – TUM, Linköping University, West Virginia University, Digital Pathology and Telepathology, Utrecht University, University of Leeds,
