Identifying and Combating Bias in Segmentation Networks by leveraging multiple resolutions
This addresses bias in medical AI for segmentation tasks, which is an incremental improvement in combating data distribution issues.
The paper tackled bias in medical image segmentation networks caused by training data at different resolutions for two groups, showing that single-resolution training leads to significant volumetric differences and erroneous segmentations (measured by DSC) for the low-resolution group. It demonstrated that multi-resolution approaches like image resampling and scale augmentation can effectively reduce these biases.
Exploration of bias has significant impact on the transparency and applicability of deep learning pipelines in medical settings, yet is so far woefully understudied. In this paper, we consider two separate groups for which training data is only available at differing image resolutions. For group H, available images and labels are at the preferred high resolution while for group L only deprecated lower resolution data exist. We analyse how this resolution-bias in the data distribution propagates to systematically biased predictions for group L at higher resolutions. Our results demonstrate that single-resolution training settings result in significant loss of volumetric group differences that translate to erroneous segmentations as measured by DSC and subsequent classification failures on the low resolution group. We further explore how training data across resolutions can be used to combat this systematic bias. Specifically, we investigate the effect of image resampling, scale augmentation and resolution independence and demonstrate that biases can effectively be reduced with multi-resolution approaches.