Abstract

In order to solve the problems of low gray scale contrast and blurred organ boundaries in some medical images, we proposed a joint algorithm of Multi-Attention Parallel CNNs and Independent Recurrent Neural Networks (MACIR) with word embedding technique combined. First, the word embedding technique is used to map the sparse spatial relation matrix into a real dense vector, which is combined with gray scale and edge matrix as input features. The multi -attention mechanism is used to add weight information to capture the importance of each feature more sensitive. Then, the Parallel Convolutional Neural Networks are used to fully exploit the deep semantic information, and IndRNN is introduced to avoid the loss of pixel hierarchy information and realize the integration of information flow. Finally, the Softmax classifier is used to complete the medical image segmentation task. Experiments showed that word embedding MACIR algorithm could effectively improve the segmentation performance of medical images on the data sets of lung X-ray and cervical CT images.

中文翻译：

---

结合词嵌入技术的多注意机制医学图像分割

摘要

为了解决某些医学图像灰度对比度低和器官边界模糊的问题，提出了一种多注意并行CNN与独立递归神经网络（MACIR）结合词嵌入技术的联合算法。首先，利用词嵌入技术将稀疏的空间关系矩阵映射为实密度向量，并将其与灰度和边缘矩阵组合作为输入特征。多注意机制用于添加权重信息，以更敏感地捕获每个特征的重要性。然后，利用并行卷积神经网络充分利用深度语义信息，引入IndRNN避免像素层次信息的丢失，实现信息流的整合。最后，Softmax分类器用于完成医学图像分割任务。实验表明，词嵌入MACIR算法可以有效提高医学图像在肺部X射线和宫颈CT图像数据集上的分割性能。