OSIC Pulmonary Fibrosis Progression

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Published in Kaggle, 2020

Competition webpage

Evaluation metric - modified version of the Laplace Log Likelihood.

Aim

The aim of this competition is to predict a patient’s severity of decline in lung function based on a CT scan of their lungs. Lung function is assessed based on output from a spirometer, which measures the forced vital capacity (FVC), i.e. the volume of air exhaled.

Data provided -

Training data

  • Baseline chest CT scan ( Week = 0 and has numerous follow up visits)
  • Associated clinical information for a set of patients. (smoking, sex, Age)
  • Entire history of FVC measurements.

Test data

  • Baseline CT scan
  • Initial FVC measurement.

Goal - Predict the final three FVC measurements for each patient, as well as a confidence value in your prediction.

Image data samples

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Dimension reduction on data using t-SNE

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Approach used -

Average weighted ensemble of EfficientNet models

Feature engineering and selection