  Following the pandemic outbreak, several works have proposed to diagnose
COVID-19 with deep learning in computed tomography (CT); reporting performance
on-par with experts. However, models trained/tested on the same in-distribution
data may rely on the inherent data biases for successful prediction, failing to
generalize on out-of-distribution samples or CT with different scanning
protocols. Early attempts have partly addressed bias-mitigation and
generalization through augmentation or re-sampling, but are still limited by
collection costs and the difficulty of quantifying bias in medical images. In
this work, we propose Mixing-AdaSIN; a bias mitigation method that uses a
generative model to generate de-biased images by mixing texture information
between different labeled CT scans with semantically similar features. Here, we
use Adaptive Structural Instance Normalization (AdaSIN) to enhance de-biasing
generation quality and guarantee structural consistency. Following, a
classifier trained with the generated images learns to correctly predict the
label without bias and generalizes better. To demonstrate the efficacy of our
method, we construct a biased COVID-19 vs. bacterial pneumonia dataset based on
CT protocols and compare with existing state-of-the-art de-biasing methods. Our
experiments show that classifiers trained with de-biased generated images
report improved in-distribution performance and generalization on an external
COVID-19 dataset.
