# Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding

arXiv: https://arxiv.org/abs/2201.11506 · HTML: https://mlsanity.com/paper/2201.11506/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Sub-Image Anomaly Detection with Deep Pyramid Correspondences](https://mlsanity.com/paper/2005.02357/) (2020)
- [Deep Anomaly Detection Using Geometric Transformations](https://mlsanity.com/paper/1805.10917/) (2018)
- [Deep Learning for Medical Anomaly Detection -- A Survey](https://mlsanity.com/paper/2012.02364/) (2020)
- [Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces](https://mlsanity.com/paper/1907.05345/) (2019)
- [Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images](https://mlsanity.com/paper/2008.03632/) (2020)
- [OCTID: Optical Coherence Tomography Image Database](https://mlsanity.com/paper/1812.07056/) (2018)
- [Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image](https://mlsanity.com/paper/1911.12527/) (2019)
- [Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis](https://mlsanity.com/paper/2007.05914/) (2020)
- [MLF-SC: Incorporating multi-layer features to sparse coding for anomaly detection](https://mlsanity.com/paper/2104.04289/) (2021)

## Built on by (most cited first, 6 of 6)

- [Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification](https://mlsanity.com/paper/2206.13123/) (2022)
- [Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification](https://mlsanity.com/paper/2204.01728/) (2022)
- [Improved Super Resolution of MR Images Using CNNs and Vision Transformers](https://mlsanity.com/paper/2207.11748/) (2022)
- [Region and Spatial Aware Anomaly Detection for Fundus Images](https://mlsanity.com/paper/2303.03817/) (2023)
- [Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis](https://mlsanity.com/paper/2211.08424/) (2022)
- [Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography](https://mlsanity.com/paper/2604.22139/) (2026)

## Abstract (arXiv)

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.