Track 5: Computational Pathology
Computational pathology refers to the application of computational techniques, including image analysis, machine learning, and data analytics, to the field of pathology. Pathology is the medical specialty that deals with the examination of tissues, organs, and bodily fluids to diagnose and understand diseases. Computational pathology leverages digital pathology, where pathology slides are digitized to create high-resolution images that can be analyzed using computer algorithms.
Key
aspects of computational pathology include:
1.
Digital Pathology: Traditional pathology involves examining tissue samples under a
microscope. In digital pathology, these samples are digitized, creating
high-resolution images that can be stored and analyzed electronically. This
allows for easier sharing of images, remote consultations, and the application
of computational techniques.
2.
Image Analysis: Computational pathology uses image
analysis algorithms to extract quantitative information from pathology images.
These algorithms can identify and quantify specific features, patterns, or
abnormalities in tissues that may be challenging for the human eye to discern.
3.
Machine Learning and Artificial Intelligence (AI): Machine learning and AI techniques are applied to
pathology data to develop algorithms that can assist pathologists in tasks such
as diagnosis, prognosis, and predicting response to treatment. These algorithms
can learn patterns from large datasets and potentially enhance the accuracy and
efficiency of pathology evaluations.
4.
Data Integration: Computational pathology involves integrating pathology data with other
clinical, molecular, and imaging data. This holistic approach allows for a more
comprehensive understanding of diseases, enabling personalized and targeted
treatment strategies.
5.
Predictive Modeling: By analyzing large datasets, computational pathology can contribute to
the development of predictive models for disease outcomes, treatment responses,
and patient prognosis.
The
application of computational pathology has the potential to improve diagnostic
accuracy, enhance efficiency, and contribute to personalized medicine. It is
particularly valuable in handling the increasing volume and complexity of
pathology data generated in modern healthcare. Researchers and practitioners in
computational pathology collaborate across disciplines, combining expertise in
pathology, computer science, and data analysis to advance the field.
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
Call
For Paper/abstract/case study: https://pathology.universeconferences.com/Computational-Pathology/
Important Information:
Conference Name: 14th Emirates Pathology, Digital Pathology & Cancer
Conference
Short Name: 14EPUCG2024
Dates: September 25-27, 2024
Venue: Holiday Inn Dubai, UAE & Online
Email: pathology@universeconferences.com
Visit: https://pathology.universeconferences.com/
Submit here: https://pathology.universeconferences.com/submit-abstract/
We
have numerous speakers and attendees from all over the world: https://pathology.universeconferences.com/committee-members-and-speakers/
Register here: https://pathology.universeconferences.com/registration/
Online Registration here: https://pathology.universeconferences.com/virtual-registration/
Call
Us: +12073070027
WhatsApp us at https://wa.me/442033222718?text=
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