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

Artificial Intelligence (1405) Clinical Information and Decision Making (1423) Machine Learning (1487) Ontologies, Classifications, and Coding (239) Diabetes Health Services and System Innovations (119) Diabetic Retinopathy (11)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52506, first published .
Impact of Gold-Standard Label Errors on Evaluating Performance of Deep Learning Models in Diabetic Retinopathy Screening: Nationwide Real-World Validation Study

Impact of Gold-Standard Label Errors on Evaluating Performance of Deep Learning Models in Diabetic Retinopathy Screening: Nationwide Real-World Validation Study

Impact of Gold-Standard Label Errors on Evaluating Performance of Deep Learning Models in Diabetic Retinopathy Screening: Nationwide Real-World Validation Study

Authors of this article:

Yueye Wang1, 2 Author Orcid Image ;   Xiaotong Han1 Author Orcid Image ;   Cong Li1 Author Orcid Image ;   Lixia Luo1 Author Orcid Image ;   Qiuxia Yin1 Author Orcid Image ;   Jian Zhang1 Author Orcid Image ;   Guankai Peng3 Author Orcid Image ;   Danli Shi2, 4 Author Orcid Image ;   Mingguang He2, 4, 5 Author Orcid Image
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Journals

  1. Seshimo H, Rashed E. Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI. Sensors 2024;24(23):7576 View