OSIC Pulmonary Fibrosis Progression
Published in Kaggle, 2020
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
Dimension reduction on data using t-SNE
Approach used -
Average weighted ensemble of EfficientNet models