# OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods

arXiv: https://arxiv.org/abs/2312.08255 · HTML: https://mlsanity.com/paper/2312.08255/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [OCTID: Optical Coherence Tomography Image Database](https://mlsanity.com/paper/1812.07056/) (2018)
- [RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation](https://mlsanity.com/paper/2209.12468/) (2022)

## Built on by (most cited first, 28 of 28)

- [EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging](https://mlsanity.com/paper/2405.11338/) (2024)
- [EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis](https://mlsanity.com/paper/2409.06644/) (2024)
- [Medical MLLM is Vulnerable: Cross-Modality Jailbreak and Mismatched Attacks on Medical Multimodal Large Language Models](https://mlsanity.com/paper/2405.20775/) (2024)
- [MadCLIP: Few-shot Medical Anomaly Detection with CLIP](https://mlsanity.com/paper/2506.23810/) (2025)
- [Improving Representation of High-frequency Components for Medical Visual Foundation Models](https://mlsanity.com/paper/2407.14651/) (2024)
- [MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis](https://mlsanity.com/paper/2506.08900/) (2025)
- [MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining](https://mlsanity.com/paper/2501.15798/) (2025)
- [FunBench: Benchmarking Fundus Reading Skills of MLLMs](https://mlsanity.com/paper/2503.00901/) (2025)
- [Continual Retinal Vision-Language Pre-training upon Incremental Imaging Modalities](https://mlsanity.com/paper/2506.19320/) (2025)
- [Conquering the Retina: Bringing Visual in-Context Learning to OCT](https://mlsanity.com/paper/2506.15200/) (2025)
- [MedicalNarratives: Connecting Medical Vision and Language with Localized Narratives](https://mlsanity.com/paper/2501.04184/) (2025)
- [MADPOT: Medical Anomaly Detection with CLIP Adaptation and Partial Optimal Transport](https://mlsanity.com/paper/2507.06733/) (2025)
- [Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers](https://mlsanity.com/paper/2504.15928/) (2025)
- [BenchReAD: A systematic benchmark for retinal anomaly detection](https://mlsanity.com/paper/2507.10492/) (2025)
- [From ML Predictions to Informed Diagnostic Assistance Using the Toulmin Model of Argumentation](https://mlsanity.com/paper/2607.09664/) (2026)
- [Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Images](https://mlsanity.com/paper/2406.16942/) (2024)
- [Inter Observer Variability Assessment through Ordered Weighted Belief Divergence Measure in MAGDM Application to the Ensemble Classifier Feature Fusion](https://mlsanity.com/paper/2409.08450/) (2024)
- [Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis](https://mlsanity.com/paper/2503.01603/) (2025)
- [PiPViT: Patch-based Visual Interpretable Prototypes for Retinal Image Analysis](https://mlsanity.com/paper/2506.10669/) (2025)
- [RetFiner: A Vision-Language Refinement Scheme for Retinal Foundation Models](https://mlsanity.com/paper/2506.22149/) (2025)
- [GCVAMD: A Modified CausalVAE Model for Causal Age-related Macular Degeneration Risk Factor Detection and Prediction](https://mlsanity.com/paper/2510.02781/) (2025)
- [EyeAgent: An Agentic AI System for Multimodal Clinical Decision Support in Ophthalmology](https://mlsanity.com/paper/2511.09394/) (2025)
- [RetinaVision: XAI-Driven Augmented Regulation for Precise Retinal Disease Classification using deep learning framework](https://mlsanity.com/paper/2602.19324/) (2026)
- [MedKCO: Medical Vision-Language Pretraining via Knowledge-Driven Cognitive Orchestration](https://mlsanity.com/paper/2603.09101/) (2026)
- [M-IDoL: Information Decomposition for Modality-Specific and Diverse Representation Learning in Medical Foundation Model](https://mlsanity.com/paper/2604.08936/) (2026)
- [Not All Forgetting Is Equal: Retention Dynamics in Fine-Tuned Image Classifiers](https://mlsanity.com/paper/2604.11508/) (2026)
- [Representation learning from OCT images](https://mlsanity.com/paper/2605.02589/) (2026)
- [Explainable Uncertainty Estimation for Reliable Medical AI](https://mlsanity.com/paper/2608.28052/) (2026)

## Abstract (arXiv)

Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset.