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Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding
[article]
2022
arXiv
pre-print
Convolutional Neural Network models have successfully detected retinal illness from optical coherence tomography (OCT) and fundus images. These CNN models frequently rely on vast amounts of labeled data for training, difficult to obtain, especially for rare diseases. Furthermore, a deep learning system trained on a data set with only one or a few diseases cannot detect other diseases, limiting the system's practical use in disease identification. We have introduced an unsupervised approach for
arXiv:2201.11506v1
fatcat:mzoezgkxjrae7f6pltgxiuhnom