ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2023
Automated Curation and AI Workflow Management System for Digital Pathology.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed, 1 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
37350884PMC10283146W4381715224What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.