ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.
AI
AI in healthcare
Automatic feature extraction
Cloud platform
Digital and Computational Pathology
FAIR
Functional tissue units
Image segmentation
Whole slide images
Journal
Proceedings of SPIE--the International Society for Optical Engineering
ISSN: 0277-786X
Titre abrégé: Proc SPIE Int Soc Opt Eng
Pays: United States
ID NLM: 101524122
Informations de publication
Date de publication:
Feb 2024
Feb 2024
Historique:
medline:
30
5
2024
pubmed:
30
5
2024
entrez:
30
5
2024
Statut:
ppublish
Résumé
Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles
Identifiants
pubmed: 38813089
doi: 10.1117/12.3008469
pmc: PMC11136532
pii:
doi:
Types de publication
Journal Article
Langues
eng