# Identifying and Categorizing Anomalies in Retinal Imaging Data

arXiv: https://arxiv.org/abs/1612.00686 · HTML: https://mlsanity.com/paper/1612.00686/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2016.

## Built on by (most cited first, 18 of 18)

- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [Student-Teacher Feature Pyramid Matching for Anomaly Detection](https://mlsanity.com/paper/2103.04257/) (2021)
- [Prototypical Residual Networks for Anomaly Detection and Localization](https://mlsanity.com/paper/2212.02031/) (2022)
- [Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2110.04538/) (2021)
- [Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images](https://mlsanity.com/paper/2308.02062/) (2023)
- [ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2103.03664/) (2021)
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- [What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Network](https://mlsanity.com/paper/2307.15860/) (2023)
- [FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks](https://mlsanity.com/paper/2301.06957/) (2023)
- [HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization](https://mlsanity.com/paper/2208.03486/) (2022)
- [Generalized Face Liveness Detection via De-fake Face Generator](https://mlsanity.com/paper/2401.09006/) (2024)
- [Restricted Generative Projection for One-Class Classification and Anomaly Detection](https://mlsanity.com/paper/2307.04097/) (2023)
- [Multi-Perspective Anomaly Detection](https://mlsanity.com/paper/2105.09903/) (2021)
- [ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network](https://mlsanity.com/paper/2112.09135/) (2021)
- [Progressive GANomaly: Anomaly detection with progressively growing GANs](https://mlsanity.com/paper/2206.03876/) (2022)
- [Unsupervised Anomaly Appraisal of Cleft Faces Using a StyleGAN2-based Model Adaptation Technique](https://mlsanity.com/paper/2211.06659/) (2022)
- [Anomaly Detection via Autoencoder Composite Features and NCE](https://mlsanity.com/paper/2502.01920/) (2025)
- [Unsupervised Anomaly Detection with an Enhanced Teacher for Student-Teacher Feature Pyramid Matching](https://mlsanity.com/paper/2512.18219/) (2025)

## Abstract (arXiv)

The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly supervised training enables the detection of findings that are known a priori. It does not scale well, and a priori definition limits the vocabulary of markers to known entities reducing the accuracy of diagnosis and prognosis. Here, we propose the identification of anomalies in large-scale medical imaging data using healthy examples as a reference. We detect and categorize candidates for anomaly findings untypical for the observed data. A deep convolutional autoencoder is trained on healthy retinal images. The learned model generates a new feature representation, and the distribution of healthy retinal patches is estimated by a one-class support vector machine. Results demonstrate that we can identify pathologic regions in images without using expert annotations. A subsequent clustering categorizes findings into clinically meaningful classes. In addition the learned features outperform standard embedding approaches in a classification task.