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Chest X-Ray Image Dicom Dataset

Dataset of X-ray images with text conclusions of radiologists and markup
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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
50
medical studies

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CASE DESCRIPTION

The dataset contains of 150 chest organ radiographs. The data format is dicom

The dataset is annotated by the professional medics for segmentation tasks. The annotation is stored in json format

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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APPLICATION AREAS OF THE DATASET

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

Automating the interpretation of chest X-rays:

Computer vision for automatic analysis and interpretation of chest X-ray images

Training Medical Personnel:

Segmentation for the education and training of medical university students, interns, and residents

Disease monitoring and control:

Computer Vision for monitoring and controlling lung pathologies and other chest conditions
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