Chest X-Ray Dataset
Dataset of X-ray images with text conclusions of radiologists and markup
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- files
- 443+
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- medical studies
- 150
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- data tags
- 13
- Medicine
- Computer Vision
- Segmentation
- Classification
- Machine Learning
-
- files
- 443+
-
- medical studies
- 150
-
- data tags
- 13
Dataset Info
| Characteristic | Data |
| Description | Chest X-ray to recognize pathologies |
| Data types | DiCOM |
| Markup | Segmentation of pathologies |
| Tasks | Pathology recognition, computer vision. |
| Total number of files | 443 |
| Number of studies | 150 |
| Labeling | ‘Nodule/mass’, ‘Dissemination’, ‘Annular shadows’, ‘Petrifications’, ‘Pleural effusion’, ‘Pneumothorax’, ‘Rib fractures’, ‘Healed rib fracture’, ‘Atelectasis’, ‘Enlarged mediastinum’, ‘Hilar enlargement’, ‘Infiltration/Consolidation’, ‘Fibrosis’ |
| Gender | Male, female |
| Age | From 25 - 70 years |
Statistics
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- Distribution by gender
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- Number of studies for each condition
Technical
Characteristics
| Characteristic | Data |
| File extension | DiCOM |
| Markup format | JSON |
Industries
Medicine and Healthcare:
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Disease diagnosis:
Using x-rays to detect lung diseases such as pneumonia and cancer.
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Telemedicine:
Using remote diagnostics to analyze x-rays and provide conclusions to patients at a distance.
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Medical text analysis:
Utilizing natural language processing to extract insights from annotation of clinical data and processing medical reports for improved patient care and decision-making
Education:
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Medical training:
Use of X-rays to educate students and professionals in radiology.
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