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Sample Efficient Lung Segmentation Using Group Structured Conditional Variational Data Imputation

Patients infected with COVID-19 can lead to their Chest X-rays (CXRs) with opacifications rendered regions, which may produce incomplete lung segmentation in automated image analysis models. To tackle this issue, we propose a Group structured Conditional Variational data Imputation model to capture the missing data accurately with conditional distribution, where the high-dimensional probability distribution is narrow down to a small latent space to account for unobserved features. This work particularly arises in the fight against COVID-19 that effectively modeling a segmentation of plausible can be presented to a subsequent automated risk scoring and treatment. We train this model with limited CXRs data to demonstrate the abilities on the task of data imputation and proved to be effective though with relatively small datasets.