Bayesian uncertainty-weighted loss for improved generalisability on
  polyp segmentation task
Rebecca S. Stone Pedro E. Chavarrias-Solano Andrew J. Bulpitt David C. Hogg Sharib Ali
While several previous studies have devised methods for segmentation of
polyps, most of these methods are not rigorously assessed on multi-center
datasets. Variability due to appearance of polyps from one center to another,
difference in endoscopic instrument grades, and acquisition quality result in
methods with good performance on in-distribution test data, and poor
performance on out-of-distribution or underrepresented samples. Unfair models
have serious implications and pose a critical challenge to clinical
applications. We adapt an implicit bias mitigation method which leverages
Bayesian epistemic uncertainties during training to encourage the model to
focus on underrepresented sample regions. We demonstrate the potential of this
approach to improve generalisability without sacrificing state-of-the-art
performance on a challenging multi-center polyp segmentation dataset (PolypGen)
with different centers and image modalities.