Hierarchical Uncertainty Estimation for Medical Image Segmentation Networks
This addresses the problem of building trustworthy segmentation models for medical imaging by handling uncertainties from noise and annotation errors, though it is incremental as it builds on existing hierarchical architectures like U-net.
The paper tackles uncertainty estimation in medical image segmentation by proposing a hierarchical method that leverages skip-connections to model multi-level uncertainties, achieving high segmentation performance and providing meaningful uncertainty maps for out-of-distribution detection.
Learning a medical image segmentation model is an inherently ambiguous task, as uncertainties exist in both images (noise) and manual annotations (human errors and bias) used for model training. To build a trustworthy image segmentation model, it is important to not just evaluate its performance but also estimate the uncertainty of the model prediction. Most state-of-the-art image segmentation networks adopt a hierarchical encoder architecture, extracting image features at multiple resolution levels from fine to coarse. In this work, we leverage this hierarchical image representation and propose a simple yet effective method for estimating uncertainties at multiple levels. The multi-level uncertainties are modelled via the skip-connection module and then sampled to generate an uncertainty map for the predicted image segmentation. We demonstrate that a deep learning segmentation network such as U-net, when implemented with such hierarchical uncertainty estimation module, can achieve a high segmentation performance, while at the same time provide meaningful uncertainty maps that can be used for out-of-distribution detection.