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

• URL: http://arxiv.org/abs/2405.14444v1
• Date: Thu, 23 May 2024 11:23:57 GMT
• Title: DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation
• Authors: Yitong Yang, Xinli Xu, Haigen Hu, Haixia Long, Qianwei Zhou, Qiu Guan,
• Abstract summary: We propose a novel framework called Dual-Branch Evi-dential Deep Learning (DuEDL) Our method significantly enhances the reliability and generalization ability of the model without sacrificing accuracy, outper-forming state-of-the-art baselines.
• Score: 2.708515419272247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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