DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation

TL;DR

DuEDL introduces a dual-branch evidential deep learning framework that improves the robustness and generalization of scribble-supervised medical image segmentation by fusing evidence from two branches for better pseudo-labels.

Contribution

The paper proposes a novel dual-branch evidential deep learning framework that enhances reliability and generalization in scribble-supervised medical image segmentation.

Findings

Significantly improves model reliability and generalization.

Outperforms state-of-the-art baselines on cardiac datasets.

Maintains high segmentation accuracy.

Abstract

Despite the recent progress in medical image segmentation with scribble-based annotations, the segmentation results of most models are still not ro-bust and generalizable enough in open environments. Evidential deep learn-ing (EDL) has recently been proposed as a promising solution to model predictive uncertainty and improve the reliability of medical image segmen-tation. However directly applying EDL to scribble-supervised medical im-age segmentation faces a tradeoff between accuracy and reliability. To ad-dress the challenge, we propose a novel framework called Dual-Branch Evi-dential Deep Learning (DuEDL). Firstly, the decoder of the segmentation network is changed to two different branches, and the evidence of the two branches is fused to generate high-quality pseudo-labels. Then the frame-work applies partial evidence loss and two-branch consistent loss for joint training of the model to adapt to the scribble supervision learning. The pro-posed method was tested on two cardiac datasets: ACDC and MSCMRseg. The results show that our method significantly enhances the reliability and generalization ability of the model without sacrificing accuracy, outper-forming state-of-the-art baselines. The code is available at https://github.com/Gardnery/DuEDL.