Commercial
Chest X-Ray Dataset
Dataset of X-ray images with text conclusions of radiologists and markup
Request a demo- Medicine The use of machine learning in the medicine
- Computer Vision Ability of a machine to interpret, analyze, and understand visual data
- Segmentation Dividing an image into semantically independent segments to determine object boundaries
- Classification Process of recognition and grouping of objects into preset categories
- 443
- files
- 150
- medical studies
Case Description
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The dataset contains of 150 chest organ radiographs. The data format is dicom
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There are 13 classes: ‘Nodule/mass’, ‘Dissemination’, ‘Annular shadows’, ‘Petrifications’, ‘Pleural effusion’, ‘Pneumothorax’, ‘Rib fractures’, ‘Healed rib fracture’, ‘Atelectasis’, ‘Enlarged mediastinum’, ‘Hilar enlargement’, ‘Infiltration/Consolidation’, ‘Fibrosis’
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The dataset is annotated by the professional medics for segmentation tasks. The annotation is stored in json format
Application areas of the dataset
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01.
Medical diagnostics:
Computer vision and classification for automatic detection and classification of various lung pathologies and other disorders of the thoracic cavity, including tumors, infections, destruction, pleural effusion, pneumothorax, and rib injuries -
02.
Automating the interpretation of chest X-rays:
Computer vision for automatic analysis and interpretation of chest X-ray images -
03.
Training Medical Personnel:
Segmentation for the education and training of medical university students, interns, and residents -
04.
Disease monitoring and control:
Computer Vision for monitoring and controlling lung pathologies and other chest conditions
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