Browser-based Data Annotation, Active Learning, and Real-Time Distribution of Artificial Intelligence Models: From Tumor Tissue Microarrays to COVID-19 Radiology.

Autor: Bhawsar PMS; Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA., Abubakar M; Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA., Schmidt MK; Division of Molecular Pathology, Netherlands Cancer Institute, Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands., Camp NJ; Huntsman Cancer Institute, University of Utah, UT 84112, USA., Cessna MH; Department of Pathology, Intermountain Healthcare Biorepository, Intermountain Healthcare, UT 84107, USA., Duggan MA; Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada., García-Closas M; Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA., Almeida JS; Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Maryland, USA.
Jazyk: angličtina
Zdroj: Journal of pathology informatics [J Pathol Inform] 2021 Sep 27; Vol. 12, pp. 38. Date of Electronic Publication: 2021 Sep 27 (Print Publication: 2021).
DOI: 10.4103/jpi.jpi_100_20
Abstrakt: Background: Artificial intelligence (AI) is fast becoming the tool of choice for scalable and reliable analysis of medical images. However, constraints in sharing medical data outside the institutional or geographical space, as well as difficulties in getting AI models and modeling platforms to work across different environments, have led to a "reproducibility crisis" in digital medicine.
Methods: This study details the implementation of a web platform that can be used to mitigate these challenges by orchestrating a digital pathology AI pipeline, from raw data to model inference, entirely on the local machine. We discuss how this federated platform provides governed access to data by consuming the Application Program Interfaces exposed by cloud storage services, allows the addition of user-defined annotations, facilitates active learning for training models iteratively, and provides model inference computed directly in the web browser at practically zero cost. The latter is of particular relevance to clinical workflows because the code, including the AI model, travels to the user's data, which stays private to the governance domain where it was acquired.
Results: We demonstrate that the web browser can be a means of democratizing AI and advancing data socialization in medical imaging backed by consumer-facing cloud infrastructure such as Box.com. As a case study, we test the accompanying platform end-to-end on a large dataset of digital breast cancer tissue microarray core images. We also showcase how it can be applied in contexts separate from digital pathology by applying it to a radiology dataset containing COVID-19 computed tomography images.
Conclusions: The platform described in this report resolves the challenges to the findable, accessible, interoperable, reusable stewardship of data and AI models by integrating with cloud storage to maintain user-centric governance over the data. It also enables distributed, federated computation for AI inference over those data and proves the viability of client-side AI in medical imaging.
Availability: The open-source application is publicly available at , with a short video demonstration at .
Competing Interests: There are no conflicts of interest.
(Copyright: © 2021 Journal of Pathology Informatics.)
Databáze: MEDLINE
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