FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification
Paper • 2407.08813 • Published
Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ValueError
Message: Multiple files found in ZIP file. Only one file per ZIP: ['oct_fundus.npy', 'slo_fundus.npy', 'age.npy', 'gender.npy', 'race.npy', 'ethnicity.npy', 'language.npy', 'marriagestatus.npy', 'md.npy', 'glaucoma.npy']
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 4195, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2533, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2711, in iter
for key, pa_table in ex_iterable.iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2249, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/csv/csv.py", line 196, in _generate_tables
csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.pd_read_csv_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/streaming.py", line 73, in wrapper
return function(*args, download_config=download_config, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1250, in xpandas_read_csv
return pd.read_csv(xopen(filepath_or_buffer, "rb", download_config=download_config), **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
return _read(filepath_or_buffer, kwds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 620, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
self._engine = self._make_engine(f, self.engine)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/parsers/readers.py", line 1880, in _make_engine
self.handles = get_handle(
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/common.py", line 805, in get_handle
raise ValueError(
ValueError: Multiple files found in ZIP file. Only one file per ZIP: ['oct_fundus.npy', 'slo_fundus.npy', 'age.npy', 'gender.npy', 'race.npy', 'ethnicity.npy', 'language.npy', 'marriagestatus.npy', 'md.npy', 'glaucoma.npy']Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card: Harvard-FairDomain
Dataset Summary
Harvard-FairDomain is a large-scale ophthalmology dataset designed for studying fairness under domain shift in medical image analysis. It supports both image segmentation and classification tasks, with 10,000 samples per task drawn from 10,000 unique patients. The dataset introduces an additional imaging modality — en-face fundus images — alongside the original scanning laser ophthalmoscopy (SLO) fundus images, enabling cross-domain fairness research.
This dataset was introduced in the ECCV 2024 paper: FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification.
Dataset Details
Dataset Description
| Field | Value |
|---|---|
| Institution | Department of Ophthalmology, Harvard Medical School |
| Tasks | Medical image segmentation, medical image classification |
| Modalities | En-face fundus image, scanning laser ophthalmoscopy (SLO) fundus image |
| Samples | 10,000 (segmentation), 10,000 (classification) |
| Patients | 10,000 per task (unique patients) |
Source Data
Harvard-FairDomain is derived from two existing Harvard ophthalmology datasets:
- Harvard-FairSeg — source for segmentation task data
- FairVLMed (FairCLIP) — source for classification task data
En-face fundus images were added to both subsets as a new imaging domain on top of the original SLO fundus images, enabling cross-domain fairness benchmarking.
Uses
Direct Use
Research on algorithmic fairness in cross-domain medical image segmentation and classification, including studies of model performance disparities across demographic groups under distribution shift.
Out-of-Scope Use
Clinical diagnosis, commercial applications, or any use prohibited by the CC BY-NC-ND 4.0 license.
Citation
BibTeX:
@article{tian2024fairdomain,
title={FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification},
author={Tian, Yu and Wen, Congcong and Shi, Min and Afzal, Muhammad Muneeb and Huang, Hao and Khan, Muhammad Osama and Luo, Yan and Fang, Yi and Wang, Mengyu},
journal={arXiv preprint arXiv:2407.08813},
year={2024}
}
APA:
Tian, Y., Wen, C., Shi, M., Afzal, M. M., Huang, H., Khan, M. O., Luo, Y., Fang, Y., & Wang, M. (2024). FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification. arXiv preprint arXiv:2407.08813.
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