Interpretability-guided Data Augmentation for Robust Segmentation in
  Multi-centre Colonoscopy Data
Valentina Corbetta Regina Beets-Tan Wilson Silva
Multi-centre colonoscopy images from various medical centres exhibit distinct
complicating factors and overlays that impact the image content, contingent on
the specific acquisition centre. Existing Deep Segmentation networks struggle
to achieve adequate generalizability in such data sets, and the currently
available data augmentation methods do not effectively address these sources of
data variability. As a solution, we introduce an innovative data augmentation
approach centred on interpretability saliency maps, aimed at enhancing the
generalizability of Deep Learning models within the realm of multi-centre
colonoscopy image segmentation. The proposed augmentation technique
demonstrates increased robustness across different segmentation models and
domains. Thorough testing on a publicly available multi-centre dataset for
polyp detection demonstrates the effectiveness and versatility of our approach,
which is observed both in quantitative and qualitative results. The code is
publicly available at:
https://github.com/nki-radiology/interpretability_augmentation