README.md

---
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

<!-- Provide a quick summary of the dataset. -->

All the images of the dataset come from this [kaggle dataset](https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k). 
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

<!-- Provide a longer summary of what this dataset is. -->
**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](https://www.kaggle.com/andrewmvd)
- **License:** MIT

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** [kaggle repo](https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k).
- **Paper:** I didn't find an associated paper, but I believe the dataset was first presented [here](https://odir2019.grand-challenge.org/dataset/).

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

Eye disease classification (single label).
Feature extraction (unsupervised or self supervised learning).

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

[More Information Needed]

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

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

<!-- Motivation for the creation of this dataset. -->

[More Information Needed]

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

[More Information Needed]

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

[More Information Needed]


#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

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

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

[More Information Needed]


## Glossary 

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

[More Information Needed]

## Dataset Card Authors 

bumbledeep