Datasets:

iraqigold
/

Medical-Modality-Dataset

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label Medical-Modality-Dataset@88f14589eba9582b4f70e7c06be16910ed93adda
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 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/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2365, in __iter__
                  example = _apply_feature_types_on_example(
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2282, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/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/python3.12/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/python3.12/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label Medical-Modality-Dataset@88f14589eba9582b4f70e7c06be16910ed93adda

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🏥 Generalized Medical Image Modality Dataset

A curated, balanced dataset for training medical imaging modality classifiers. Contains images from four modalities (CT, MRI, X-Ray, Ultrasound) spanning multiple anatomical regions to ensure robust generalization.

Total images: 9,450 | Target: 9,450

📊 Modality Summary

Modality	Organ Classes (for Organ Classifier)	Images	Target	% of Total
CT	Head, Chest, Abdomen	2,200	2,200	23.3%
MRI	Brain, Spine	2,000	2,000	21.2%
US	Breast, Kidney, Ovary_Pelvis	2,250	2,250	23.8%
XRAY	Chest, Hand, Knee	3,000	3,000	31.7%
TOTAL		9,450	9,450	100%

🔬 Per-Organ Breakdown

Modality	Organ	Images	Target	Kaggle Source	Status
CT	Abdomen	1,000	1,000	nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone	✅
CT	Chest	1,000	1,000	mohamedhanyyy/chest-ctscan-images	✅
CT	Head	200	200	felipekitamura/head-ct-hemorrhage	✅
MRI	Brain	1,000	1,000	masoudnickparvar/brain-tumor-mri-dataset	✅
MRI	Spine	1,000	1,000	anoukstein/spider-mri-spine-t2-png	✅
US	Breast	750	750	aryashah2k/breast-ultrasound-images-dataset	✅
US	Kidney	750	750	gurjeetkaurmangat/kidney-ultrasound-images-stone-and-no-stone	✅
US	Ovary_Pelvis	750	750	orvile/mmotu-ovarian-ultrasound-images-dataset	✅
XRAY	Chest	1,000	1,000	paultimothymooney/chest-xray-pneumonia	✅
XRAY	Hand	1,000	1,000	antonbudnychuk/hand-xray	✅
XRAY	Knee	1,000	1,000	shashwatwork/knee-osteoarthritis-dataset-with-severity	✅

📁 Folder Structure

modality_dataset/
├── CT/
│   ├── Chest/        (CT chest scans)
│   ├── Head/         (CT head hemorrhage scans)
│   └── Abdomen/      (CT kidney / abdominal scans)
├── MRI/
│   ├── Brain/        (Brain tumor MRI)
│   └── Spine/        (Spine T2 MRI)
├── XRAY/
│   ├── Chest_Pneumonia/  (Chest X-ray — pneumonia)
│   ├── Chest_TB/         (Chest X-ray — tuberculosis)
│   └── Hand/             (Hand X-ray)
└── US/
    ├── Breast/       (Breast ultrasound)
    ├── Kidney/       (Kidney ultrasound)
    └── Ovary_Pelvis/ (Ovarian / pelvic ultrasound)

🚀 Usage

Images are packaged in modality_dataset.zip. Extract and use directly:

import zipfile
with zipfile.ZipFile("modality_dataset.zip", "r") as z:
    z.extractall("./modality_dataset")

The top-level folder names (CT, MRI, XRAY, US) are the class labels for the modality classifier. Sub-folders represent the anatomical regions used as data sources.

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