Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
KC-Cancer (train + test)
Lung-cancer CT dataset packaged as two tar archives to avoid HuggingFace per-file rate limits when downloading:
train.tar(≈8.3 GB) —kc_cancer_final_toy: 2000 cases (1000 cancer + 500 benign + 500 healthy), single-annotator polygon labels rasterized to per-lesion maskstest.tar(≈847 MB) —kc_cancer_4.3_test_toy: 252-case multi-annotator held-out test set with biopsy ground truth
Both use the same NIfTI + CSV toy schema. After extracting, each tarball gives a folder with:
{train,test}/
├── images/{case_id}.nii.gz # 3D CT (int16, HU)
├── lesions/{case_id}/L{k}/ANNOT_*.nii.gz # binary mask per lesion (per annotator for test)
├── clinical.csv # one row per case
├── lesions.csv # one row per lesion (per annotator for test)
└── README.md # column-level documentation + caveats
Quick start
from huggingface_hub import hf_hub_download
import tarfile, pandas as pd, nibabel as nib
# download + extract train
train_tar = hf_hub_download(
repo_id="phinvabc123/kc-cancer-final",
filename="train.tar",
repo_type="dataset",
)
with tarfile.open(train_tar) as t:
t.extractall(path="./kc_cancer_data")
clin = pd.read_csv("./kc_cancer_data/train/clinical.csv")
les = pd.read_csv("./kc_cancer_data/train/lesions.csv")
v = nib.load(f"./kc_cancer_data/train/images/{clin.case_id.iloc[0]}.nii.gz").get_fdata()
print(v.shape, v.dtype)
Same recipe for test.tar.
Recommended labels
- train:
case_class(cancer / benign / healthy) or binarylabel(1 = cancer, 0 = else). Cohort-derived. - test:
effective_label(252/252 coverage; prioritizes biopsy ground truth). Filtereffective_label_source == 'biopsy'for the 164 biopsy-confirmed cases.
Lesion volume
lesions.csv ships 4 volume columns; volume_mm3_real_z is recommended
(trapezoidal integration with real DICOM Z positions; handles non-uniform
slice gaps correctly). See train/README.md for details.
License & acknowledgements
CC-BY-NC-4.0. Contributing hospitals: Bạch Mai, Đại học Y Hà Nội, Bắc Giang, Bắc Ninh, Bệnh viện K.
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