Track 2: Digital Pathology

Call for Abstract/ Research Paper:

Sub Tracks: 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

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.

WHO SHOULD ATTEND?

Trainee pathologists, Haematologists, Clinical scientists in the field of molecular diagnosis, Consultants, Trainees in Haematology Consultants, Trainee histopathologists, Medical students interested in Histopathology, Pathologists, Scientists, PhD students & post-doctoral scientists researching in pathology, Foundation doctors & undergraduates interested in pathology, Biomedical Scientists, Doctors, Clinical Practitioners, Physicians, Research Scientists, Medical Education Professionals, Laboratory Managers and Supervisors, Clinical Laboratory Scientists, Medical Technologists, Students, Hematopathologists, Dermatopathologists, Surgical Pathologists, Oncologists, Surgeons