README.md

---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label_code
    dtype: int64
  - name: label
    dtype: string
  splits:
  - name: train
    num_bytes: 6672857783.54
    num_examples: 35108
  download_size: 6535643877
  dataset_size: 6672857783.54
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/tanlikesmath/diabetic-retinopathy-resized). 
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. -->

The EyePACS dataset consists of retinal images originally published in the Kaggle competition ["Diabetic Retinopathy Detection"](https://www.kaggle.com/c/diabetic-retinopathy-detection/overview).
This version includes a subset of the original data, specifically the publicly available training images.


- **Funded by:** California Healthcare Foundation
- **Shared by:** [ilovescience](https://www.kaggle.com/tanlikesmath)
- **License:** MIT

### Dataset Sources

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

Retinal images were provided by EyePACS, a free platform for retinopathy screening.

- **Repository:**
  - [kaggle dataset](https://www.kaggle.com/datasets/tanlikesmath/diabetic-retinopathy-resized)
  - [Original location (full dataset)](https://www.kaggle.com/c/diabetic-retinopathy-detection/)

## Uses

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

### Direct Use

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

Diabetic retinopathy classification (binary or multiclass).
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. -->

There are no predefined partitions in this dataset; it is up to the user to decide how to split the data.

## Dataset Creation

### Curation Rationale

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

*Resizing*: The images were resized to 1024x1024 if their dimensions exceeded this size; otherwise, they remain unchanged.

*Cropping*: The black space around the fundus images was cropped by identifying the center and radius of the circle. Some images were either entirely black or almost fully black, with no mask detected. 
These images were manually removed. However, there may still be some noisy images left.

For more detailed information, see the description of the [kaggle dataset](https://www.kaggle.com/datasets/tanlikesmath/diabetic-retinopathy-resized).

### 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. -->

The images in the dataset come from different models and types of cameras, which can affect the visual appearance of left vs. right. 
Some images are shown as one would see the retina anatomically (macula on the left, optic nerve on the right for the right eye). 
Others are shown as one would see through a microscope condensing lens (i.e. inverted, as one sees in a typical live eye exam).

#### 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. -->

- EyePACS, the platform that provided the retinal images.
- Emma Dugas, Jared, Jorge, and Will Cukierski, the creators of the kaggle competition and the dataset.


#### Annotation process

A clinician has rated the presence of diabetic retinopathy in each image on a scale of 0 to 4, according to the following scale:

0 - No DR

1 - Mild

2 - Moderate

3 - Severe

4 - Proliferative DR

#### 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. -->

This dataset contains **only the resized and cropped images** from [kaggle](https://www.kaggle.com/datasets/tanlikesmath/diabetic-retinopathy-resized), not the original files.

### 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. -->

Emma Dugas, Jared, Jorge, and Will Cukierski. Diabetic Retinopathy Detection. https://kaggle.com/competitions/diabetic-retinopathy-detection, 2015. Kaggle.

## 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