dataset_info:
features:
- name: patient_id
dtype: int64
- name: age
dtype: int64
- name: sex
dtype: string
- name: label
dtype: string
- name: image
dtype: image
- name: label_code
dtype: int64
splits:
- name: train
num_bytes: 401186219.968
num_examples: 6392
download_size: 397265540
dataset_size: 401186219.968
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
Dataset Card for Dataset Name
All the images of the dataset come from this kaggle dataset. Some minor modifications have been made to the metadata. All credit goes to the original authors and the contributor on Kaggle.
Dataset Details
Dataset Description
Ocular Disease Intelligent Recognition (ODIR) is a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes and doctors' diagnostic keywords from doctors.
This dataset is meant to represent "real-life" set of patient information collected by Shanggong Medical Technology Co., Ltd. from different hospitals/medical centers in China. In these institutions, fundus images are captured by various cameras in the market, such as Canon, Zeiss and Kowa, resulting into varied image resolutions.
- Created by:
- Peking University
- National Institute of Health Data Science at Peking University (NIHDS-PKU)
- Institute of Artificial Intelligence at Peking University（IAI-PKU）
- Shanggong Medical Technology Co., Ltd
- Advanced Institute of Information Technology at Peking University(AIIT-PKU)
- Shared by: Larxel
- License: MIT
Dataset Sources
- Repository: kaggle repo.
- Paper: I didn't find an associated paper, but I believe the dataset was first presented here.
Uses
Direct Use
Eye disease classification (single label). Feature extraction (unsupervised or self supervised learning).
Out-of-Scope Use
[More Information Needed]
Dataset Structure
Based on the information provided by the original authors: The 5,000 patients in this challenge are divided into training, off-site testing and on-site testing subsets. Almost 4,000 cases are used in training stage while others are for testing stages.
The proportion of images per category in training and testing datasets is given in the following table:
| No. | Labels | Training Cases | Off-site Testing Cases | On-site Testing Cases | All Cases |
|---|---|---|---|---|---|
| 1 | N | 1,135 | 161 | 324 | 1,620 |
| 2 | D | 1,131 | 162 | 323 | 1,616 |
| 3 | G | 207 | 30 | 58 | 307 |
| 4 | C | 211 | 32 | 64 | 243 |
| 5 | A | 171 | 25 | 47 | 295 |
| 6 | H | 94 | 14 | 30 | 138 |
| 7 | M | 177 | 23 | 49 | 249 |
| 8 | O | 944 | 134 | 268 | 1,346 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Data Collection and Processing
[More Information Needed]
Who are the source data producers?
[More Information Needed]
Annotation process
Annotations are labeled by trained human readers with quality control management. They classify patient into eight labels including:
- normal (N)
- diabetes (D)
- glaucoma (G)
- cataract (C)
- AMD (A)
- hypertension (H)
- myopia (M)
- other diseases/abnormalities (O)
based on both eye images and additionally patient age.
Personal and Sensitive Information
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation
[More Information Needed]
Glossary
[More Information Needed]
Dataset Card Authors
bumbledeep