Call for Papers
Track: 5 Computational Pathology
SUB TRACK Computational Pathology, computational analysis, diagnose disease, automatically, Whole slide image, machine learning, deep learning, artificial intelligence, image analysis, histopathological glass slide, microscope, slide scanners, scanners, techniques, digital image analysis, diagnostics. precise diagnoses, patient-specific treatments, disease pathogenesis, disease stratification, data technologies, tissue features, individual cells, inference, prediction algorithms, laboratory personnel,
Computational pathology
A field of pathology that uses computer analysis to examine patient samples using a wide range of techniques in order to understand disease. This work focuses on information extraction from digital pathology images and their accompanying meta-data, often using AI techniques like deep learning.
The fields of pathology and laboratory medicine are being revolutionised by improvements in high-throughput laboratory and health information technologies. A crucial and significant development in the provision of healthcare will undoubtedly be the capacity to extract clinically useful knowledge using computational methods from complex, high-dimensional laboratory and clinical (digital) data, leading to more accurate diagnoses, disease stratification, and patient-specific treatment selection.
Using a variety of raw data sources, such as clinical electronic medical records, laboratory data, including “-omics,” and imaging, computational pathology is an approach to diagnosis.
Pathology can enhance the dissemination of medically useful knowledge over time and across populations and boost the effectiveness of health care delivery by creating the tools to harness those drives.
Bright field and fluorescence scanners are used by the Computational Pathology Laboratory (DCPL) to acquire high resolution images of tissue slides, allowing researchers to see specifics of the morphologic and spectral properties of cells and/or tumour regions.
Use of advanced computational techniques
A crucial component of the promise of CPATH has been the employment of cutting-edge computational techniques like deep learning (DL) and machine learning (ML). Artificial intelligence is demonstrated by both ML and its subset DL (AI). An associated idea is ML-powered image analysis, which enables incredibly precise image classification or segmentation. Once these picture attributes are connected with other sorts of patient information than the image itself, the outputs of these computer-based tools could then be included in a comprehensive CPATH process. Despite the fact that this kind of research shows enormous promise for a paradigm shift in healthcare, numerous obstacles still stand in the way of its widespread clinical application.
A pathologist is a doctor who analyses body parts and bodily tissues. Additionally, he or she is in charge of running lab testing. A pathologist is a crucial part of the treatment team who assists other medical professionals in making diagnosis.
Pathology Society European Society for Digital and Integrative Pathology, Austrian Society of Pathology ,Belgian Society of Pathology ,The European Society of Pathology, Danish Pathology Society, Czech Society of Pathology, French Society of Pathology ,German Society for Pathology, Brazilian Society of Pathology, Austrian society of pathology
Computational Pathology University University Pathology, University of South Alabama, Department of Pathology | UCL Cancer Institute, Institute of Biochemical Plant Pathology, Division of Pathology – Karolinska Institute, Department of Pathology – University of Zurich, Department of Pathology, , Therapeutic pathology – Lund University, PATHOLOGY – University of Washington, National Institute of Pathology
