OSIC Pulmonary Fibrosis Progression Dataset

Dataset Overview
Data 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.

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