OSIC Pulmonary Fibrosis Progression Dataset
Dataset Overview
Add to BookmarkData Type | Image | Default Task | Classification, Regression, Clustering |
---|---|---|---|
Attribute Type | Real | Published Year | 2019 |
Area of Dataset | Life | Missing Values | No |
No. of Instances | 34300 | No. of Attribute | 17 |
Dataset Description:
The Open Source Imaging Consortium (OSIC) is proud to partner with Kaggle to host the first-ever computational challenge for interstitial lung diseases: The OSIC Pulmonary Fibrosis Progression Challenge. A $55,000 prize will be offered to the Kaggle investigator(s) who devises the highest performing algorithm.
In this competition, you’ll predict a patient’s severity of the decline in lung function based on a CT scan of their lungs. You’ll determine lung function based on output from a spirometer, which measures the volume of air inhaled and exhaled. The challenge is to use machine learning techniques to make a prediction with the image, metadata, and baseline FVC as input.
If successful, patients and their families would better understand their prognosis when they are first diagnosed with this incurable lung disease. Improved severity detection would also positively impact treatment trial design and accelerate the clinical development of novel treatments.
Summary:-
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