Abstract
End-to-end deep learning has been demonstrated to exhibit human-level performance in many retinal image analysis tasks. However, such models’ generalizability to data from new sources may be less than optimal. We highlight some benefits of introducing intermediate goals in deep learning-based models.
Cite
CITATION STYLE
APA
Lim, G., Hsu, W., & Lee, M. L. (2019). Intermediate Goals in Deep Learning for Retinal Image Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11367 LNCS, pp. 276–281). Springer Verlag. https://doi.org/10.1007/978-3-030-21074-8_22
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