WEBINAR ANNOUNCEMENT: USING DEEP LEARNING TO RECOGNIZE COMMON PATTERNS OF INJURY IN GASTRIC PATHOLOGY
In this 30 minute webinar, researchers and pathologists from the University of New Mexico will discuss their recently published study using the HALO AI deep learning convolutional neural network to detect and classify different types of gastric injury.
AI as been widely studied in neoplastic pathology but this technology also holds promise in the realm of inflammatory pathology. Pathologists at the University of New Mexico recently published a paper describing how a CNN can accurately discriminate between the three most common histologic patterns in medical gastric biopsies, with a focus on H. pylori infection. This has the potential to improve practice efficiency in the future. The webinar will include:
1) A brief introduction to the histologic features of H. pylori gastritis, reactive gastropathy, and normal gastric mucosa
2) An overview of the Methods presented in the aforementioned paper
3) A detailed description of the Results and potential future applications
PRESENTERS
Joshua A. Hanson MD
University of New Mexico School of Medicine
Associate Professor, Department of Pathology
Interim Division Chief, Anatomic Pathology
David R. Martin MD
University of New Mexico School of Medicine
Assistant Professor, Department of Pathology
Fred Schultz, MA
University of New Mexico School of Medicine
Informatics Manager, Department of Pathology
CLICK HERE TO REGISTER
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Indica Labs software solutions provide fast, quantitative evaluation of whole slide tissues using HALO and HALO AI for image analysis and HALO Link to manage images, data, and facilitate collaboration. With unmatched ease-of-use and scalability, pharmaceutical, healthcare, and research organizations worldwide are using HALO for high-throughput, whole-slide image quantification in areas such as oncology, immuno-oncology, neuroscience, ophthalmology, metabolism, respiratory, and toxicological pathology.
Souce: Indica Labs
