Anomaly Detection In Retinal Images Using Multi-Scale Deep Feature Sparse Coding

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 sys...

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Veröffentlicht in:Proceedings (International Symposium on Biomedical Imaging) S. 1 - 5
Hauptverfasser: Das, Sourya Dipta, Dutta, Saikat, Shah, Nisarg A., Mahapatra, Dwarikanath, Ge, Zongyuan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 28.03.2022
Schlagworte:
Anomaly detection
Autoencoder
Convolutional neural networks
Deep Multi-scale Feature
Encoding
Feature extraction
Retina
Sparse Coding
Task analysis
Training
Transfer Learning
Transformers
ISSN:1945-8452
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Zusammenfassung: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 detecting anomalies in retinal images to overcome this issue. We have proposed a simple, memory efficient, easy to train method which followed a multi-step training technique that incorporated autoencoder training and Multi-Scale Deep Feature Sparse Coding (MDFSC), an extended version of normal sparse coding, to accommodate diverse types of retinal datasets. We achieve relative AUC score improvement of 7.8%, 6.7% and 12.1% over state-of-the-art SPADE on Eye-Q, IDRiD and OCTID datasets respectively.
ISSN:1945-8452
DOI:10.1109/ISBI52829.2022.9761713