论文标题
DLUNET：基于半监督学习的双光线UNET用于多器官分段
DLUNet: Semi-supervised Learning based Dual-Light UNet for Multi-organ Segmentation
论文作者
论文摘要
腹部多器官的手动基础真理是劳动密集型的。为了充分利用CT数据，我们开发了一个基于半监督的双光线UNET。在训练阶段，它由两个光UNET组成，它们通过使用一致的基于基于的学习，可以同时使用标签和未标记的数据。此外，引入了可分离的卷积和残留串联，以降低计算成本。此外，还采用了强大的分割损失来提高性能。在推理阶段，仅使用光UNET，需要低时间成本和更少的GPU内存利用率。验证集中该方法的平均DSC为0.8718。该代码可在https://github.com/laihaoran/semi-supervisennunet中找到。
The manual ground truth of abdominal multi-organ is labor-intensive. In order to make full use of CT data, we developed a semi-supervised learning based dual-light UNet. In the training phase, it consists of two light UNets, which make full use of label and unlabeled data simultaneously by using consistent-based learning. Moreover, separable convolution and residual concatenation was introduced light UNet to reduce the computational cost. Further, a robust segmentation loss was applied to improve the performance. In the inference phase, only a light UNet is used, which required low time cost and less GPU memory utilization. The average DSC of this method in the validation set is 0.8718. The code is available in https://github.com/laihaoran/Semi-SupervisednnUNet.
