Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling
Author:
Funder
U.S. Department of Health & Human Services | NIH | National Eye Institute
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41746-024-01207-4.pdf
Reference55 articles.
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3. Lin, M. et al. Improving model fairness in image-based computer-aided diagnosis. Nat. Commun. 14, 6261 (2023).
4. Chen, Q. et al. Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration. J. Am. Med. Inform. Assoc. 28, 1135–1148 (2021).
5. Peng, Y. et al. DeepSeeNet: A Deep Learning Model for Automated Classification of Patient-based Age-related Macular Degeneration Severity from Color Fundus Photographs. Ophthalmology 126, 565–575 (2019).
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