论文标题
跨制造商胸部X射线分段的双编码器融合U-NET（Fefu-NET）
Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-ray Segmentation
论文作者
论文摘要
许多基于深度学习的方法已应用于医学图像细分，并实现了最先进的性能。由于胸部X射线数据在研究Covid-19中的重要性，因此需要对能够精确分割胸部X射线上的软组织进行最新模型。用于探索最佳分割模型的数据集来自2014年开放的蒙哥马利和深圳医院。最著名的技术是U-NET，它已用于包括Chest X射线在内的许多医疗数据集。但是，大多数变体U网络主要集中在上下文信息和跳过连接的提取上。仍然有很大的空间来改善空间特征的提取。在本文中，我们提出了一个基于Inception卷积神经网络的双重编码器融合U-NET框架，该框架具有扩张，密集连接的复发性卷积神经网络，该卷积神经网络被称为fefu-net。密集连接的复发路径将网络扩展到更深层次，以促进上下文特征提取。为了增加网络的宽度并丰富了特征的表示，采用了扩张的开始块。启动块可以从各种接受场中捕获全球和局部空间信息。同时，通过求和功能融合了这两条路径，从而保留了解码部分的上下文和空间信息。这个多学习尺度的模型受益于来自两个不同制造商（蒙哥马利和深圳医院）的胸部X射线数据集。与基本的U-NET，残留的U-NET，BCDU-NET，R2U-NET和注意力R2U-NET相比，Fefu-NET的性能更好。该模型证明了混合数据集并使用最先进的方法。该建议的框架的源代码是公共https://github.com/uceclz0/defu-net。
A number of methods based on deep learning have been applied to medical image segmentation and have achieved state-of-the-art performance. Due to the importance of chest x-ray data in studying COVID-19, there is a demand for state-of-the-art models capable of precisely segmenting soft tissue on the chest x-rays. The dataset for exploring best segmentation model is from Montgomery and Shenzhen hospital which had opened in 2014. The most famous technique is U-Net which has been used to many medical datasets including the Chest X-rays. However, most variant U-Nets mainly focus on extraction of contextual information and skip connections. There is still a large space for improving extraction of spatial features. In this paper, we propose a dual encoder fusion U-Net framework for Chest X-rays based on Inception Convolutional Neural Network with dilation, Densely Connected Recurrent Convolutional Neural Network, which is named DEFU-Net. The densely connected recurrent path extends the network deeper for facilitating contextual feature extraction. In order to increase the width of network and enrich representation of features, the inception blocks with dilation are adopted. The inception blocks can capture globally and locally spatial information from various receptive fields. At the same time, the two paths are fused by summing features, thus preserving the contextual and spatial information for decoding part. This multi-learning-scale model is benefiting in Chest X-ray dataset from two different manufacturers (Montgomery and Shenzhen hospital). The DEFU-Net achieves the better performance than basic U-Net, residual U-Net, BCDU-Net, R2U-Net and attention R2U-Net. This model has proved the feasibility for mixed dataset and approaches state-of-the-art. The source code for this proposed framework is public https://github.com/uceclz0/DEFU-Net.
