Fu Qiang, Dong Hongbin
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China.
Entropy (Basel). 2022 Oct 27;24(11):1543. doi: 10.3390/e24111543.
Deep neural networks have been successfully applied in the field of image recognition and object detection, and the recognition results are close to or even superior to those from human beings. A deep neural network takes the activation function as the basic unit. It is inferior to the spiking neural network, which takes the spiking neuron model as the basic unit in the aspect of biological interpretability. The spiking neural network is considered as the third-generation artificial neural network, which is event-driven and has low power consumption. It modulates the process of nerve cells from receiving a stimulus to firing spikes. However, it is difficult to train spiking neural network directly due to the non-differentiable spiking neurons. In particular, it is impossible to train a spiking neural network using the back-propagation algorithm directly. Therefore, the application scenarios of spiking neural network are not as extensive as deep neural network, and a spiking neural network is mostly used in simple image classification tasks. This paper proposed a spiking neural network method for the field of object detection based on medical images using the method of converting a deep neural network to spiking neural network. The detection framework relies on the YOLO structure and uses the feature pyramid structure to obtain the multi-scale features of the image. By fusing the high resolution of low-level features and the strong semantic information of high-level features, the detection precision of the network is improved. The proposed method is applied to detect the location and classification of breast lesions with ultrasound and X-ray datasets, and the results are 90.67% and 92.81%, respectively.
深度神经网络已成功应用于图像识别和目标检测领域，其识别结果接近甚至优于人类。深度神经网络以激活函数为基本单元。在生物可解释性方面，它不如以脉冲神经元模型为基本单元的脉冲神经网络。脉冲神经网络被认为是第三代人工神经网络，它是事件驱动的，且功耗低。它对神经细胞从接收刺激到发放脉冲的过程进行调制。然而，由于脉冲神经元不可微，直接训练脉冲神经网络很困难。特别是，不可能直接使用反向传播算法训练脉冲神经网络。因此，脉冲神经网络的应用场景不如深度神经网络广泛，脉冲神经网络大多用于简单的图像分类任务。本文提出了一种基于医学图像目标检测领域的脉冲神经网络方法，该方法采用将深度神经网络转换为脉冲神经网络的方法。检测框架依赖于YOLO结构，并使用特征金字塔结构来获取图像的多尺度特征。通过融合低级特征的高分辨率和高级特征的强语义信息，提高了网络的检测精度。所提出的方法应用于利用超声和X射线数据集检测乳腺病变的位置和分类，结果分别为90.67%和92.81%。
