Automated Curation and AI Workflow Management System for Digital Pathology.

Autor:	Bumgardner VKC; University of Kentucky, Lexington, Kentucky., Armstrong S; University of Kentucky, Lexington, Kentucky., Virodov A; University of Kentucky, Lexington, Kentucky., Hickey C; University of Kentucky, Lexington, Kentucky.
Jazyk:	angličtina
Zdroj:	AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science [AMIA Jt Summits Transl Sci Proc] 2023 Jun 16; Vol. 2023, pp. 71-80. Date of Electronic Publication: 2023 Jun 16 (Print Publication: 2023).
Abstrakt:	Digital pathology applications present several challenges, including the processing, storage, and distribution of gigapixel images across distributed computational resources and viewing stations. Individual slides must be available for interactive review, and large repositories must be programmatically accessible for dataset and model building. We present a platform to manage and process multi-modal pathology data (images and case information) across multiple locations. Using an agent-based system coupled with open-source automated machine learning and review tools allows not only dynamic load-balancing and cross-network operation but also the development of research and clinical AI models using the data managed by the platform. The platform presented covers end-to-end AI workflow from data acquisition and curation through model training and evaluation allowing for sharing and review. We conclude with a case study of colon and prostate cancer model development utilizing the presented system. (©2023 AMIA - All rights reserved.)
Databáze:	MEDLINE
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