size
dict | objects
listlengths 1
2
|
|---|---|
{
"height": 1360,
"width": 1360
} | [
{
"geometryType": "polygon",
"labelerLogin": "labeler01",
"classTitle": "Petrifications",
"points": {
"exterior": [
[
453,
143
],
[
456,
157
],
[
470,
163
],
[
467,
148
],
[
467,
138
],
[
460,
137
]
],
"interior": []
}
}
] |
{
"height": 1524,
"width": 1524
} | [
{
"geometryType": "polygon",
"labelerLogin": "labeler01",
"classTitle": "Infiltration/Consolidation",
"points": {
"exterior": [
[
586,
955
],
[
708,
952
],
[
711,
952
],
[
756,
845
],
[
753,
503
],
[
697,
375
],
[
679,
357
],
[
646,
354
],
[
610,
354
],
[
533,
417
],
[
461,
488
],
[
414,
572
],
[
345,
738
],
[
348,
851
],
[
384,
896
],
[
479,
938
],
[
586,
955
]
],
"interior": []
}
},
{
"geometryType": "polygon",
"labelerLogin": "labeler01",
"classTitle": "Infiltration/Consolidation",
"points": {
"exterior": [
[
1197,
988
],
[
1211,
988
],
[
1277,
944
],
[
1283,
935
],
[
1298,
887
],
[
1298,
860
],
[
1271,
664
],
[
1229,
589
],
[
1158,
536
],
[
1063,
512
],
[
1036,
512
],
[
1003,
521
],
[
955,
557
],
[
955,
589
],
[
955,
664
],
[
961,
720
],
[
1006,
896
],
[
1024,
911
],
[
1095,
970
],
[
1197,
988
]
],
"interior": []
}
}
] |
{
"height": 1360,
"width": 1360
} | [
{
"geometryType": "polygon",
"labelerLogin": "labeler01",
"classTitle": "Nodule/mass",
"points": {
"exterior": [
[
941,
269
],
[
934,
278
],
[
932,
287
],
[
932,
297
],
[
932,
304
],
[
938,
309
],
[
947,
310
],
[
957,
306
],
[
961,
298
],
[
964,
289
],
[
964,
283
],
[
961,
275
],
[
956,
269
],
[
950,
268
]
],
"interior": []
}
}
] |
{
"height": 1360,
"width": 1360
} | [
{
"geometryType": "polygon",
"labelerLogin": "labeler01",
"classTitle": "Fibrosis",
"points": {
"exterior": [
[
878,
566
],
[
913,
552
],
[
941,
563
],
[
976,
583
],
[
1001,
575
],
[
973,
558
],
[
948,
545
],
[
904,
540
],
[
869,
542
]
],
"interior": []
}
},
{
"geometryType": "polygon",
"labelerLogin": "labeler01",
"classTitle": "Fibrosis",
"points": {
"exterior": [
[
965,
478
],
[
1020,
465
],
[
1057,
458
],
[
1091,
429
],
[
955,
462
]
],
"interior": []
}
}
] |
{
"height": 1360,
"width": 1360
} | [
{
"geometryType": "polygon",
"labelerLogin": "labeler01",
"classTitle": "Infiltration/Consolidation",
"points": {
"exterior": [
[
583,
689
],
[
522,
736
],
[
488,
734
],
[
454,
752
],
[
401,
776
],
[
396,
841
],
[
456,
889
],
[
517,
904
],
[
580,
908
],
[
641,
866
],
[
653,
778
],
[
653,
720
]
],
"interior": []
}
}
] |
Chest X-rays, DICOM Data and Segmentation
This dataset consists of 150 medical studies with chest X-ray (CXR) images primarily focused on the detection of lung diseases, including COVID-19 cases and pneumonias. The collection includes frontal chest radiographs and chest radiography scans in DICOM format. The dataset is ideal for medical research, disease detection, and classification tasks, particularly for developing computer-aided diagnosis and machine learning models - Get the data
The dataset features annotations and segmentation results, with lung segmentations provided by radiologists and medical experts. These annotations are useful for training deep learning algorithms to improve classification performance in identifying common diseases and lung abnormalities.
13 classes of labeling:
- Petrifications
- Nodule/mass
- Infiltration/Consolidation
- Fibrosis
- Dissemination
- Pleural effusion
- Hilar enlargement
- Annular shadows
- Healed rib fracture
- Enlarged medinastium
- Rib fractures
- Pneumothorax
- Atelectasis
💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.
Metadata for the dataset
Researchers can leverage this dataset for clinical practice, studying imaging data for better early detection methods and computer-aided screening. It offers valuable segmentation methods and labels, making it suitable for tasks such as image classification and segmentation. The dataset is well-suited for developing learning models and diagnostic systems, providing essential tools for medical imaging and clinical information gathering.
Content
The dataset includes:
- dicoms: includes x-ray scans in .dcm format,
- annotations: includes annotations in JSON format made for files in the previous folder,
- .csv file: includes links to the fies and metadata
The dataset is compiled represents some of the newest and high-quality chest radiographs available, ensuring that the imaging data is both original and highly useful for radiologists and medical researchers alike.
🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects
- Downloads last month
- 39