DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation
<|reference_start|>DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised Medical Image Segmentation: 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.<|reference_end|>
@article{yang2024duedl:,
    title={DuEDL: Dual-Branch Evidential Deep Learning for Scribble-Supervised
  Medical Image Segmentation},
    author={Yitong Yang, Xinli Xu, Haigen Hu, Haixia Long, Qianwei Zhou, Qiu Guan},
    journal={arXiv preprint arXiv:2405.14444},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14444},
    primaryClass={cs.CV}
}