  Many current neural networks for medical imaging generalise poorly to data
unseen during training. Such behaviour can be caused by networks overfitting
easy-to-learn, or statistically dominant, features while disregarding other
potentially informative features. For example, indistinguishable differences in
the sharpness of the images from two different scanners can degrade the
performance of the network significantly. All neural networks intended for
clinical practice need to be robust to variation in data caused by differences
in imaging equipment, sample preparation and patient populations.
  To address these challenges, we evaluate the utility of spectral decoupling
as an implicit bias mitigation method. Spectral decoupling encourages the
neural network to learn more features by simply regularising the networks'
unnormalised prediction scores with an L2 penalty, thus having no added
computational costs.
  We show that spectral decoupling allows training neural networks on datasets
with strong spurious correlations and increases networks' robustness for data
distribution shifts. To validate our findings, we train networks with and
without spectral decoupling to detect prostate cancer tissue slides and
COVID-19 in chest radiographs. Networks trained with spectral decoupling
achieve up to 9.5 percent point higher performance on external datasets.
  Our results show that spectral decoupling helps with generalisation issues
associated with neural networks, and can be used to complement or replace
computationally expensive explicit bias mitigation methods, such as stain
normalization in histological images. We recommend using spectral decoupling as
an implicit bias mitigation method in any neural network intended for clinical
use.
