Datasets:

universitytehran
/

MRI-GBM-MET

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label MRI-GBM-MET@15d7ee33630a90fbe0e8090f515bcd9ea1caec8b
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label MRI-GBM-MET@15d7ee33630a90fbe0e8090f515bcd9ea1caec8b

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MRI-GBM-MET: Brain MRI Dataset (GBM vs. MET)

Dataset Summary

The MRI-GBM-MET dataset is a collection of axial brain MRI scans from 90 human subjects diagnosed with either Glioblastoma (GBM) or Brain Metastasis (MET). It is designed to support medical image classification, diagnostic research, and multimodal evaluation tasks.

All subject-level diagnostic labels are histopathologically confirmed (post-surgical biopsy or resection) and are completely independent of radiological masks. The dataset includes pixel-level expert annotations from board-certified neuroradiologists used to identify lesion-containing slices.

Dataset Configurations

For each subject and modality (T1CE and T2), four distinct configurations are provided:

Configuration	Description
FullVolume	The complete axial sequence as acquired during the MRI scan.
ROISlices	Full-frame axial slices that contain the expert-annotated lesion.
ROICrops	Localized views of the lesion area, cropped from ROISlices with a standardized margin.
NonROI	Slices from the FullVolume that do not contain any annotated lesion (negative control).

Key Statistics

Total Subjects: 90 (45 Glioblastoma, 45 Brain Metastasis)
Total Slices: 4,091 images in .jpg format
- T1-weighted Contrast-Enhanced (T1CE) MRI: 2,199 slices
- T2-weighted MRI: 1,892 slices
Sequence Length per Subject: Median of 20 slices (range 15–160)
ROI Slices per Subject:
- T1CE: Median of 8 slices
- T2: Median of 11 slices

Technical Specifications & Metadata

Format: All images are in .jpg format.
Naming Convention: Files are named as slice_{index}.jpg where {index} represents the absolute global sequence position within the original scan.
Consistency: Filenames are aligned across configurations. If a lesion is located on slice 10, the corresponding image slice_009.jpg is present in FullVolume, ROISlices, and ROICrops.
Integrity Rule: For any subject sequence:
$$\text{Total Slices (FullVolume)} = \text{ROISlices count} + \text{NonROI count}$$

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