# Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data

arXiv: https://arxiv.org/abs/1810.13404 · HTML: https://mlsanity.com/paper/1810.13404/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

## Built on by (most cited first, 11 of 11)

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- [Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration](https://mlsanity.com/paper/2301.04525/) (2023)
- [Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition](https://mlsanity.com/paper/2505.05768/) (2025)
- [Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)](https://mlsanity.com/paper/2405.09549/) (2024)
- [Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms](https://mlsanity.com/paper/2206.04311/) (2022)
- [Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information](https://mlsanity.com/paper/2105.00463/) (2021)
- [Detection of Emerging Infectious Diseases in Lung CT based on Spatial Anomaly Patterns](https://mlsanity.com/paper/2410.19535/) (2024)
- [Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey](https://mlsanity.com/paper/2504.11588/) (2025)
- [AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction-Based Semantic Patch-Scoring](https://mlsanity.com/paper/2509.12905/) (2025)
- [SMT-AD: a scalable quantum-inspired anomaly detection approach](https://mlsanity.com/paper/2604.06265/) (2026)
- [Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomography](https://mlsanity.com/paper/2604.22139/) (2026)

## Abstract (arXiv)

The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation of training examples by experts. However, supervision does not scale well, due to the amount of necessary training examples, and the limitation of the marker vocabulary to known entities. In this proof-of-concept study, we propose unsupervised identification of anomalies as candidates for markers in retinal Optical Coherence Tomography (OCT) imaging data without a constraint to a priori definitions. We identify and categorize marker candidates occurring frequently in the data, and demonstrate that these markers show predictive value in the task of detecting disease. A careful qualitative analysis of the identified data driven markers reveals how their quantifiable occurrence aligns with our current understanding of disease course, in early- and late age-related macular degeneration (AMD) patients. A multi-scale deep denoising autoencoder is trained on healthy images, and a one-class support vector machine identifies anomalies in new data. Clustering in the anomalies identifies stable categories. Using these markers to classify healthy-, early AMD- and late AMD cases yields an accuracy of 81.40%. In a second binary classification experiment on a publicly available data set (healthy vs. intermediate AMD) the model achieves an area under the ROC curve of 0.944.