Datasets
Models
Downloads: 1
Data:
Image
Specialty:
Pediatrics
Radiology
Medical Imaging Technique:
X-Ray
Medical Imaging Region:
Chest
Demographics:
0-17
Clinical Purpose:
Diagnosis
Task:
Classification
License:
Creative Commons Attribution 4.0
Image
Specialty:
Pediatrics
Radiology
Medical Imaging Technique:
X-Ray
Medical Imaging Region:
Chest
Demographics:
0-17
Clinical Purpose:
Diagnosis
Task:
Classification
License:
Creative Commons Attribution 4.0
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| a/README.md | b/README.md | ||
|---|---|---|---|
| 1 | <div class="sc-kdrUpr eZtUed"><div class="sc-UEtKG dGqiYy sc-hDzlxo bEIZRR"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-fHzVOS cUYeeo"><div class="sc-davvxH flNyFK"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-jCNfQM igJSrG"><h3>Context</h3> |
1 | <div class="sc-kdrUpr eZtUed"><div class="sc-UEtKG dGqiYy sc-hDzlxo bEIZRR"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">Pediatric Pneumonia</h2></div></div></div><div class="sc-fHzVOS cUYeeo"><div class="sc-davvxH flNyFK"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-jCNfQM igJSrG"><h3>Context</h3> |
| 2 | 2 | ||
| 3 | <p>Pediatric chest X-rays are harder to properly acquire and standardize when compared to adults, as for a children in a dark room, with people watching them from a glass window with strange machinery doesn't make for a comfortable experience.<br> |
3 | <p>Pediatric chest X-rays are harder to properly acquire and standardize when compared to adults, as for a children in a dark room, with people watching them from a glass window with strange machinery doesn't make for a comfortable experience.<br> |
| 4 | At the same time, children present a different physiology that is important to be captured in X-ray classification algorithms, as most datasets tend to focus on adults only.</p> |
4 | At the same time, children present a different physiology that is important to be captured in X-ray classification algorithms, as most datasets tend to focus on adults only.</p> |
| 5 | 5 | ||
| 6 | <h3>Content</h3> |
6 | <h3>Content</h3> |
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| 15 | <h3>How to cite this dataset:</h3> |
15 | <h3>How to cite this dataset:</h3> |
| 16 | 16 | ||
| 17 | <p>Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2018), “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification”, Mendeley Data, v2<br> |
17 | <p>Kermany, Daniel; Zhang, Kang; Goldbaum, Michael (2018), “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification”, Mendeley Data, v2<br> |
| 18 | <a rel="noreferrer nofollow" aria-label="http://dx.doi.org/10.17632/rscbjbr9sj.2 (opens in a new tab)" target="_blank" href="http://dx.doi.org/10.17632/rscbjbr9sj.2">http://dx.doi.org/10.17632/rscbjbr9sj.2</a></p> |
18 | <a rel="noreferrer nofollow" aria-label="http://dx.doi.org/10.17632/rscbjbr9sj.2 (opens in a new tab)" target="_blank" href="http://dx.doi.org/10.17632/rscbjbr9sj.2">http://dx.doi.org/10.17632/rscbjbr9sj.2</a></p> |
| 19 | </div></div></div> |
19 | </div></div></div> |