  Reliably detecting diseases using relevant biological information is crucial
for real-world applicability of deep learning techniques in medical imaging. We
debias deep learning models during training against unknown bias - without
preprocessing/filtering the input beforehand or assuming specific knowledge
about its distribution or precise nature in the dataset. We use control regions
as surrogates that carry information regarding the bias, employ the classifier
model to extract features, and suppress biased intermediate features with our
custom, modular DecorreLayer. We evaluate our method on a dataset of 952 lung
computed tomography scans by introducing simulated biases w.r.t. reconstruction
kernel and noise level and propose including an adversarial test set in
evaluations of bias reduction techniques. In a moderately sized model
architecture, applying the proposed method to learn from data exhibiting a
strong bias, it near-perfectly recovers the classification performance observed
when training with corresponding unbiased data.
