HisStar / Harvard-FairSeg

[ICLR 24] Harvard FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling

Harvard-FairSeg

[ICLR'24] Harvard FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling

by Yu Tian*, Min Shi*, Yan Luo*, Ava Kouhana, Tobias Elze, and Mengyu Wang.

Download Harvard-FairSeg Dataset

• Our Harvard-FairSeg dataset can be downloaded via this link.

• Alternatively, you could also use this Google Drive link to directly download our Harvard-FairSeg dataset.

• Please refer to each of the folders for FairSeg with SAMed and TransUNet, respectively.

• CVer中文讲解

Dataset Description

This dataset can only be used for non-commercial research purposes. At no time, the dataset shall be used for clinical decisions or patient care. The data use license is CC BY-NC-ND 4.0.

The dataset contains 10,000 patients includes 10,000 SLO fundus images. The cup-disc mask, patient age, sex, race, language, marital status, and ethnicity information are also included in the data.

10,000 SLO fundus images with pixel-wise cup-disc masks are in the Google Drive folder: data_00001.npz data_00002.npz ... data_10000.npz

NPZ files have the following keys:

fundus_slo: SLO fundus image
disc_cup_borders: cup-disc mask for the corresponding SLO fundus image
age: patient's age
race: 0 - Asian, 1 - Black, 2 - White
gender: 0 - Female, 1 - Male
ethnicity: 0 - Non-Hispanic, 1 - Hispanic
language: 0 - English, 1 - Spanish, 2 - Others
marriagestatus: 0 - Married, 1 - Single, 2 - Divorced, 3 - Widowed, 4 - Leg-Sep

More Fairness Datasets

• 🍻🍻 For more fairness datasets including 2D and 3D images of three different eye diseases, please check our dataset webpage!

Acknowledgement & Citation

If you find this repo useful for your research, please consider citing our paper:

@inproceedings{tian2024fairseg,
      title={Harvard FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling}, 
      author={Yu Tian, Min Shi, Yan Luo, Ava Kouhana, Tobias Elze, Mengyu Wang},
      booktitle={International Conference on Learning Representations (ICLR)},
      year={2024},
}