bumbledeep/smdg-full-dataset

Dataset Card for Dataset Name

All the images of the dataset come from this kaggle dataset. Only fundus images have been collected and 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

Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) is a collection and standardization of 19 public datasets, comprised of full-fundus glaucoma images and associated basic image metadata. This dataset is designed to be exploratory and open-ended with multiple use cases. This dataset is the largest public repository of fundus images with glaucoma.

The objective of this dataset is a machine learning-ready dataset for glaucoma-related applications. Using the help of the community, new open-source glaucoma datasets will be reviewed for standardization and inclusion in this dataset.

• Curated by: Riley Kiefer
• Shared by: Riley Kiefer
• License: MIT

Dataset Sources

• Repository:
  • kaggle repo
  • Github repo
• Paper: There is no specific paper associated with the dataset, but the author has contributed to closely related papers (see citation).

Uses

Direct Use

Glaucoma classification.

Out-of-Scope Use

[More Information Needed]

Dataset Structure

Dataset	0 (Non-Glaucoma)	1 (Glaucoma)	-1 (Glaucoma Suspect)
BEH (Bangladesh Eye Hospital)	463	171	0
CRFO-v4	31	48	0
DR-HAGIS (Diabetic Retinopathy, Hypertension, Age-related macular degeneration and Glacuoma ImageS)	0	10	0
DRISHTI-GS1-TRAIN	18	32	0
DRISHTI-GS1-TEST	13	38	0
EyePACS-AIROGS	0	3269	0
FIVES	200	200	0
G1020	724	296	0
HRF (High Resolution Fundus)	15	15	0
JSIEC-1000 (Joint Shantou International Eye Center)	38	0	13
LES-AV	11	11	0
OIA-ODIR-TRAIN	2932	197	18
OIA-ODIR-TEST-ONLINE	802	58	25
OIA-ODIR-TEST-OFFLINE	417	36	9
ORIGA-light	482	168	0
PAPILA	333	87	68
REFUGE1-TRAIN (Retinal Fundus Glaucoma Challenge 1 Train)	360	40	0
REFUGE1-VALIDATION (Retinal Fundus Glaucoma Challenge 1 Validation)	360	40	0
sjchoi86-HRF	300	101	0
Total	7499	4817	133

The original dataset is not splitted. Training, validation and test partitions were created randomly with proportions 70:15:15.

Dataset Creation

Curation Rationale

• Full fundus images (and corresponding segmentation maps) are standardized using a novel algorithm (Citation 1) by cropping the background, centering the fundus image, padding missing information, and resizing to 512x512 pixels. This standardization ensures that the most amount of foreground information is prevalent during the resizing process for machine-learning-ready image processing.
• Each available metadata text is standardized by provided each fundus image as a row and each fundus attribute as a column in a CSV file

Source Data

Data Collection and Processing

[More Information Needed]

Who are the source data producers?

Researchers and creators of the 19 datasets contained in SMDG-19.

Personal and Sensitive Information

[More Information Needed]

Bias, Risks, and Limitations

The original dataset includes optic disc segmentation, optic cup segmentation, blood vessel segmentation, and any more per-instance text metadata. The version hosted here only contains fundus images.

Recommendations

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

Citation

BibTeX:

SMDG; @dataset{smdg, title={SMDG, A Standardized Fundus Glaucoma Dataset}, url={https://www.kaggle.com/ds/2329670}, DOI={10.34740/KAGGLE/DS/2329670}, publisher={Kaggle}, author={Riley Kiefer}, year={2023} }

Related papers

1. Kiefer, Riley, et al. "A Catalog of Public Glaucoma Datasets for Machine Learning Applications: A detailed description and analysis of public glaucoma datasets available to machine learning engineers tackling glaucoma-related problems using retinal fundus images and OCT images." Proceedings of the 2023 7th International Conference on Information System and Data Mining. 2023.

2. R. Kiefer, M. Abid, M. R. Ardali, J. Steen and E. Amjadian, "Automated Fundus Image Standardization Using a Dynamic Global Foreground Threshold Algorithm," 2023 8th International Conference on Image, Vision and Computing (ICIVC), Dalian, China, 2023, pp. 460-465, doi: 10.1109/ICIVC58118.2023.10270429.

3. Kiefer, Riley, et al. "A Catalog of Public Glaucoma Datasets for Machine Learning Applications: A detailed description and analysis of public glaucoma datasets available to machine learning engineers tackling glaucoma-related problems using retinal fundus images and OCT images." Proceedings of the 2023 7th International Conference on Information System and Data Mining. 2023.

4. R. Kiefer, J. Steen, M. Abid, M. R. Ardali and E. Amjadian, "A Survey of Glaucoma Detection Algorithms using Fundus and OCT Images," 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 2022, pp. 0191-0196, doi: 10.1109/IEMCON56893.2022.9946629.

Glossary

[More Information Needed]

More Information

[More Information Needed]

Dataset Card Authors

bumbledeep