论文标题
Segtransvae：混合CNN-带有医疗图像分割的正规化变压器
SegTransVAE: Hybrid CNN -- Transformer with Regularization for medical image segmentation
论文作者
论文摘要
当前关于医学图像分割深度学习的研究暴露了他们学习全球语义信息或本地上下文信息的局限性。为了解决这些问题，本文提出了一个名为Segtransvae的新型网络。 segtransvae建立在编码器架构上，用变量自动编码器（VAE）分支来利用变压器到网络，以与分段共同重建输入图像。据我们所知，这是结合了CNN，Transformer和VAE成功的第一种方法。在最近引入的各种数据集上的评估表明，Segtransvae的表现优于以前的骰子分数和$ 95 \％$ - Haudorff距离的方法，而与简单的推理时间与简单的基于CNN的架构网络具有可比性。源代码可在以下网址获得：https：//github.com/itruonghai/segtransvae。
Current research on deep learning for medical image segmentation exposes their limitations in learning either global semantic information or local contextual information. To tackle these issues, a novel network named SegTransVAE is proposed in this paper. SegTransVAE is built upon encoder-decoder architecture, exploiting transformer with the variational autoencoder (VAE) branch to the network to reconstruct the input images jointly with segmentation. To the best of our knowledge, this is the first method combining the success of CNN, transformer, and VAE. Evaluation on various recently introduced datasets shows that SegTransVAE outperforms previous methods in Dice Score and $95\%$-Haudorff Distance while having comparable inference time to a simple CNN-based architecture network. The source code is available at: https://github.com/itruonghai/SegTransVAE.
