Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data
This work addresses the challenge of domain variability in medical imaging for clinicians and researchers, though it is incremental as it builds on existing data augmentation techniques.
The paper tackles the problem of poor generalizability of deep segmentation networks across multi-centre colonoscopy images by introducing an interpretability-guided data augmentation method, which improves robustness and achieves effective results in polyp detection as shown on a public dataset.
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