This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (3229) Machine Learning (2076) Artificial Intelligence (2129) Decision Support for Health Professionals (1539) Imaging Informatics (302) Tools, Programs and Algorithms (376) Ontologies, Classifications, and Coding (324) AI applications in genomics and pathology informatics, including ‘Generative AI’ (19) Cancer Prognosis Models and Machine Learning (67) Colon and Rectal Cancer (50) Innovations and Technology for Cancer Prevention and Screening (201)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67457, first published .
Automating Colon Polyp Classification in Digital Pathology by Evaluation of a “Machine Learning as a Service” AI Model: Algorithm Development and Validation Study

Automating Colon Polyp Classification in Digital Pathology by Evaluation of a “Machine Learning as a Service” AI Model: Algorithm Development and Validation Study

Automating Colon Polyp Classification in Digital Pathology by Evaluation of a “Machine Learning as a Service” AI Model: Algorithm Development and Validation Study

Authors of this article:

David Beyer1 Author Orcid Image ;   Evan Delancey2 Author Orcid Image ;   Logan McLeod3 Author Orcid Image
Article Authors Cited by (1) Tweetations (1) Metrics

Journals

  1. Qin Y, Chen X, Wang Y, An Y. HSF-Net: Hybrid spatial and frequency domain transformer network for polyp segmentation. Neurocomputing 2026;664:132100 View