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MedHK23
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IMT-CXR

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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 ({'atelectasis, right&base', '0.1', '0.2'}) and 5 missing columns ({'Answer', 'Unnamed: 0', 'Instruction', 'ID', 'Task'}).

This happened while the csv dataset builder was generating data using

hf://datasets/MedHK23/IMT-CXR/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 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, 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 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, 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 1417, 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 1049, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, 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 1741, 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 1872, 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 ({'atelectasis, right&base', '0.1', '0.2'}) and 5 missing columns ({'Answer', 'Unnamed: 0', 'Instruction', 'ID', 'Task'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/MedHK23/IMT-CXR/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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