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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'0.1', '0.2', 'atelectasis, right&base'}) and 5 missing columns ({'Answer', 'Instruction', 'Unnamed: 0', 'ID', 'Task'}).
This happened while the csv dataset builder was generating data using
hf://datasets/MedHK23/OmniFM-Dr/MIMIC_classification-location_train.tsv (at revision a6a26a6d66a4972de315c142888742b404387ab8)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
0: int64
0.1: int64
0.2: int64
atelectasis, right&base: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 710
to
{'Unnamed: 0': Value(dtype='int64', id=None), '0': Value(dtype='int64', id=None), 'ID': Value(dtype='int64', id=None), 'Instruction': Value(dtype='string', id=None), 'Answer': Value(dtype='string', id=None), 'Task': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1396, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1045, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1884, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2015, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'0.1', '0.2', 'atelectasis, right&base'}) and 5 missing columns ({'Answer', 'Instruction', 'Unnamed: 0', 'ID', 'Task'}).
This happened while the csv dataset builder was generating data using
hf://datasets/MedHK23/OmniFM-Dr/MIMIC_classification-location_train.tsv (at revision a6a26a6d66a4972de315c142888742b404387ab8)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
Unnamed: 0
int64 | 0
int64 | ID
int64 | Instruction
string | Answer
string | Task
string |
|---|---|---|---|---|---|
0 | 1 | 0 | describe the image | extensive subcutaneous emphysema involving the entire chest and lower neck is unchanged . evaluation of the lungs is limited due to linear opacities from subcutaneous air collections . within this limitation, a small right apical pneumothorax likely persists . pleural fluid is small in amount, if any . there is increased opacification of the the right lung base, likely reflecting collapse . the cardiomediastinal contours are within normal limits . extensive pneumomediastinum is not significantly changed from . . persistent tiny right apical pneumothorax . . no significant change in pneumomediastinum . . extensive subcutaneous emphysema, similar to prior exam . | report generation |
1 | 2 | 1 | Is Mass in this image? | no Mass. | classification |
2 | 3 | 2 | describe the image | no focal consolidation is seen . there is minimal biapical pleural thickening . no pleural effusion or pneumothorax is seen . the cardiac and mediastinal silhouettes are unremarkable . external jewelry overlie the lower chest . no acute cardiopulmonary process . no focal consolidation to suggest pneumonia . | report generation |
3 | 4 | 3 | give the accurate bbox of Cardiomegaly | 342,539,690,729 | localization |
4 | 5 | 4 | describe the image | been no change since in moderate cardiomegaly, pulmonary vascular engorgement, and small bilateral pleural effusions . no pneumothorax . | report generation |
5 | 6 | 5 | describe the image | left chest wall pacer device is again seen with leads extending to the region of the right atrium and right ventricle unchanged and with an intact appearance . midline sternotomy wires and mediastinal clips are again noted . the cardiomediastinal silhouette remains prominent though not significantly changed . low lung volumes limit the assessment . there is mild pulmonary edema with probable small bilateral pleural effusions . no pneumothorax . bony structures are intact . cardiomegaly, mild edema, small bilateral pleural effusions . | report generation |
6 | 7 | 6 | describe the image | persistent cardiomegaly accompanied by worsening pulmonary vascular congestion and mild-to-moderate pulmonary edema as well as enlarging right pleural effusion, now moderate in size and associated with adjacent atelectasis in the right mid and lower lung regions . focal rounded opacity lateral to the left infrahilar region may represent a focus of coalescing edema, but differential diagnosis includes focal aspiration and developing infection . attention to this region on a short-term followup radiograph is suggested . | report generation |
7 | 8 | 7 | please segment the heart from the given image. | 203,319,196,299,197,278,202,256,207,233,210,213,216,199,227,192,238,187,249,187,261,187,271,186,285,194,304,209,329,229,354,246,374,265,386,291,384,317,367,335,344,345,320,350,295,350,271,349,248,345,222,335,203,319,196,299,197,278,202,256 | segmentation |
8 | 9 | 8 | give the accurate bbox of Cardiomegaly | 579,348,843,717 | localization |
Dataset Description
Our OmniFM-Dr framework introduces a multi-task chest x-ray dataset, which is used for the joint training of disease classification, localization, segmentation, and report generation. This dataset comprises various publicly available datasets, such as MIMIC-CXR, VinDr-CXR, and ChestX-Det10. For each image, it can potentially contribute to multiple tasks, such as report generation and classification.
NOTE: Due to requirements related to data compliance and other regulations, the dataset is temporarily unavailable. However, for each task, we will provide a showcase of five samples.
Dataset Details
- MIMIC: contains more than 377,110 radiograph images from over 227,835 radiographic studies. Each radiograph is paired with lesion classification and associated radiology report. It is used for multi-label classification and report generation tasks.
- Padchest: includes 160,840 images obtained from 67,000 patients, covering six different position views. Different radiographic findings were labeled and used for the classification task in this study.
- CXR-AL14: is a large-scale dataset for chest X-ray image detection. It has more than 140,000 chest X-ray radiographs containing 253,844 bounding boxes in 14 chest abnormal object categories.
- VinDr-CXR: includes chest radiographs with annotations for the classification of 28 common chest diseases. The dataset contains 15,000 CXR scans in the training set. We select eight diseases from the dataset along with their corresponding bounding box for the disease localization task.
- ChestX-Det: consists of 3,578 images from NIH ChestXray14 for 13 common disease. We select seven diseases from the dataset along with bounding box for the disease localization task.
- CheXmask: contains 676,803 lung and heart segmentation masks of chest images from six publicly available databases: CANDID-PTX, ChestXray14, Chexpert, MIMIC-CXR, Padchest, and VinDr-CXR. We include 224,316 data for training and 10,000 data from ChestXray14 for downstream evaluation.
- SIIM: comes from the SIIM-ACR Pneumothorax Segmentation competition and contains 12,090 images, among which approximately 3,000 cases are positive for pneumothorax disease with masks.
Dataset Structure
- MIMIC:
- MIMIC_classification_report-generation_xxx.tsv: is used for classification and report generation tasks. For each row, it contains id, report, "label1 && label2", subject_id, study_id, dicom_id.
- MIMIC_classification-location_xxx.tsv: is used for location vqa task. For each row, it contains id, "label1,severity && label2, severity", subject_id, study_id, dicom_id.
- MIMIC_classification-severity_xxx.tsv: is used for severity vqa task. For each row, it contains id, "label, location1 & location2", subject_id, study_id, dicom_id.
- Padchest:
- Padchest_classification_xxx.tsv: is used for classification task. For each row, it contains id, "label1 && label2", subject_id, study_id, dicom_id.
- CXR-AL14:
- CXR_AL14_localization_xxx.tsv: is used for locatization and classification tasks. For each row, it contains id, label, "x1,y1,x2,y2", image_id.
- VinDr-CXR:
- VinDr_CXR_localization_xxx.tsv: is used for locatization and classification tasks. For each row, it contains id, label, "x1,y1,x2,y2", image_id.
- ChestX-Det:
- ChestX_Det_localization_xxx.tsv: is used for locatization and classification tasks. For each row, it contains id, label, "x1,y1,x2,y2", image_id.
- CheXmask:
- CheXmask_segmentation_xxx.tsv: is used for segmentation task. For each row, it contains id, label, "x1,y1,x2,y2, ..., x30, y30", subject_id, study_id, dicom_id.
- SIIM:
- SIIM_segmentation_xxx.tsv: is used for segmentation task. For each row, it contains id, label, "x1,y1,x2,y2, ..., x30, y30", subject_id, study_id, dicom_id.
Dataset Use
- Please run data_prepare.py, which constructs a training batch for all tasks. Each row should contain the following: id, instruction, label, image_id, and task_type.
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