[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.

• 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

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

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

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},
}