NIH Chest X-rays Bbox version
NIH Chest X-rays Bbox version
Datasets
NIH Chest X-rays Bbox version
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NIH Chest X-rays Bbox version
Use Case
NIH Chest X-rays Bbox version
Description
Explore the NIH Chest X-ray Dataset, featuring 112,120 annotated X-ray images from 30,805 patients. Leveraging Natural Language Processing for label accuracy, this dataset supports advanced research in medical imaging and weakly-supervised learning, overcoming challenges in chest X-ray diagnosis.
Description:
The NIH Chest X-ray Dataset is a significant resource for medical imaging research, addressing the challenges of diagnosing chest X-rays, which can be more complex than chest CT scans. Previously, Openi held the largest publicly available collection of chest X-ray images with 4,143 images. However, this NIH dataset expands the field dramatically, offering 112,120 X-ray images from 30,805 unique patients.
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To generate the disease labels for this dataset, researchers utilized Natural Language Processing to extract disease classifications from the corresponding radiological reports. This process ensures that the labels are over 90% accurate, making them highly suitable for weakly-supervised learning. While the original radiology reports are not publicly available, you can find detailed information about the labeling process in the Open Access paper: “ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases” by Wang et al.
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