Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images
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Be sure to download the most recent version of this dataset to maintain accuracy. This dataset contains thousands of validated OCT and Chest X-Ray images described and analyzed in "Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning". The images are split into a training set and a testing set of independent patients. Images are labeled as (disease)-(randomized patient ID)-(image number by this patient) and split into 4 directories: CNV, DME, DRUSEN, and NORMAL. This repository of images is made available for use in research only. How to cite this data: Kermany D, Goldbaum M, Cai W et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018; 172(5):1122-1131. doi:10.1016/j.cell.2018.02.010.
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PneumoniaMNIST is a 2D dataset based on a prior dataset of 5,856 pediatric chest X-Ray images, for binary-class classification of pneumonia against normal.
“The PneumoniaMNIST is based on a prior dataset22,23 of 5,856 pediatric chest X-Ray images. The task is binary-class classification of pneumonia against normal.”
From MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification (2023) · cites this paper as [23]
OCTMNIST is a 2D dataset based on a prior dataset of 109,309 optical coherence tomography images for retinal diseases, comprising 4 diagnosis categories for multi-class classification.
“The OCTMNIST is based on a prior dataset22,23 of 109,309 valid optical coherence tomography (OCT) images for retinal diseases. The dataset is comprised of 4 diagnosis categories, leading to a multi-class classification task.”
From MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification (2023) · cites this paper as [23]
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Cited by 172
- Authors
- Kermany, D.
- Corresponding author
- Kermany, Daniel
- ORCID
- Kermany, Daniel
- Venue
- Mendeley
- Publisher
- Mendeley
- Published
- 2018-06-01
- Volume
- 3
- OpenAlex
- W2807876212
- Cited by
- 172
- References
- 0
- Citations by year
- 2026: 13 · 2025: 24 · 2024: 33 · 2023: 24 · 2022: 32 · 2021: 33 · 2020: 12 · 2019: 1
- FWCI
- 7.95
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Mendeley Data (cc-by)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- optical coherence tomography, chest radiography, retinal disease, choroidal neovascularization, diabetic macular edema, drusen, disease classification, diagnostic imaging, deep learning
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