Identifying and Combating Bias in Segmentation Networks by leveraging multiple resolutions

TL;DR

This paper investigates how resolution bias affects segmentation network predictions in medical imaging and demonstrates that multi-resolution training strategies can significantly reduce systematic bias and improve segmentation accuracy.

Contribution

It introduces a novel analysis of resolution bias in medical segmentation and proposes multi-resolution training methods to mitigate this bias.

Findings

Single-resolution training causes significant bias and segmentation errors.

Multi-resolution approaches effectively reduce bias and improve segmentation accuracy.

Resampling and scale augmentation enhance model robustness across resolutions.

Abstract

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.