Zhang Ran, Duan Huichuan, Cheng Jiezhi, Zheng Yuanjie
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1552-1555. doi: 10.1109/EMBC44109.2020.9175919.
The introduction of deep learning techniques for the computer-aided detection scheme has shed a light for real incorporation into the clinical workflow. In this work, we focus on the effect of attention in deep neural networks on the classification of tuberculosis x-ray images. We propose a Convolutional Block Attention Module (CBAM), a simple but effective attention module for feed-forward convolutional neural networks. Given an intermediate feature map, our module infers attention maps and multiplied it to the input feature map for adaptive feature refinement. It achieves high precision and recalls while localizing objects with its attention. We validate the performance of our approach on a standard-compliant data set, including a dataset of 4990 x-ray chest radiographs from three hospitals and show that our performance is better than the models used in previous work.
将深度学习技术引入计算机辅助检测方案为真正融入临床工作流程带来了曙光。在这项工作中，我们专注于深度神经网络中的注意力对肺结核X光图像分类的影响。我们提出了一种卷积块注意力模块（CBAM），这是一种用于前馈卷积神经网络的简单而有效的注意力模块。给定一个中间特征图，我们的模块推断注意力图并将其与输入特征图相乘，以进行自适应特征细化。它在通过注意力定位对象的同时实现了高精度和召回率。我们在一个符合标准的数据集上验证了我们方法的性能，该数据集包括来自三家医院的4990张胸部X光片，并表明我们的性能优于先前工作中使用的模型。
