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XA-170K

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label XA-170K@d126a47e9ddcc44dd231dbdf0b298065e982ff34
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2092, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2197, in cast_table_to_features
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1995, in cast_array_to_feature
                  return feature.cast_storage(array)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1169, in cast_storage
                  [self._strval2int(label) if label is not None else None for label in storage.to_pylist()]
                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1098, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label XA-170K@d126a47e9ddcc44dd231dbdf0b298065e982ff34

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Dataset of ''Vascular Anatomy-aware Self-supervised Pre-training for X-ray Angiogram Analysis''.

Authors: De-Xing Huang^1,2, Chaohui Yu³, Xiao-Hu Zhou^1,2, Tian-Yu Xiang^1,2, Qin-Yi Zhang^1,2, Mei-Jiang Gui^1,2, Rui-Ze Ma¹, Chen-Yu Wang¹, Nu-Fang Xiao¹, Fan Wang³, and Zeng-Guang Hou^1,2

¹ Institute of Automation, Chinese Academy of Sciences

² University of Chinese Academy of Sciences

³ DAMO Acamedy, Alibaba Group

XA-170K is collected from four publicly available sources: CADICA, SYNTAX, XCAD, and CoronaryDominance.

i) CADICA comprises coronary angiography videos from 42 patients, with durations ranging from 1 to 151 frames. From these, we select 6,594 high-quality frames.

ii) SYNTAX contains 2,943 X-ray angiograms derived from 231 patients.

iii) XCAD provides a set of 1,747 angiograms, from which 1,621 images are utilized.

iv) CoronaryDominance consists of videos from 1,574 patients. We extract informative frames from each video sequence, yielding a total of 160,320 images.

✏️ Citation

If you utilize the pre-training dataset, please also consider citing the original data sources:

@article{jimenez2024cadica,
  title={CADICA: A new dataset for coronary artery disease detection by using invasive coronary angiography},
  author={Jim{\'e}nez-Partinen and others},
  journal={Expert Systems},
  volume={41},
  number={12},
  pages={e13708},
  year={2024}
}

@article{mahmoudi2025x,
  title={X-ray Coronary Angiogram images and {SYNTAX} score to develop Machine-Learning algorithms for {CHD} Diagnosis},
  author={Mahmoudi, Seyed Sajjad and others},
  journal={Scientific Data},
  volume={12},
  number={1},
  pages={471},
  year={2025}
}

@inproceedings{ma2021self,
  title={Self-supervised vessel segmentation via adversarial learning},
  author={Ma, Yuxin and others},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  pages={7536--7545},
  year={2021}
}

@article{kruzhilov2025coronarydominance,
  title={{CoronaryDominance}: Angiogram dataset for coronary dominance classification},
  author={Kruzhilov, Ivan and others},
  journal={Scientific Data},
  volume={12},
  number={1},
  pages={341},
  year={2025}
}

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Paper for waha2000huang/XA-170K

Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysis

Paper • 2602.11536 • Published Feb 12