论文标题
卷积神经网络的不同随机选择的激活层的比较，用于医疗保健
Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare
论文作者
论文摘要
生物图像的分类是在许多领域的重要应用，例如细胞表型识别，检测细胞细胞器和组织病理学分类的重要任务，它可能有助于早期医学诊断，从而允许自动疾病分类而无需人类专家。在本文中，我们使用神经网络的集合对生物医学图像进行了分类。我们使用RESNET50体系结构创建此合奏，并通过将其替换为其他功能来修改其激活层。我们在以下激活中选择我们的激活：relu，泄漏的relu，参数relu，Elu，Adaptive PieceWice线性单元，S形relu，Swish，Swish，Mish，Mish，Missin线性单元，高斯线性线性单元，参数可变形线性线性单元，软根符号（SRS）和其他。 作为基线，我们使用了仅使用Relu激活的神经网络集合。我们在几个中小型生物医学图像数据集上测试了我们的网络。我们的结果证明，我们最好的合奏比幼稚的方法获得了更好的性能。为了鼓励这项工作的可重复性，所有实验的MATLAB代码将在https://github.com/lorilisnanni共享。
Classification of biological images is an important task with crucial application in many fields, such as cell phenotypes recognition, detection of cell organelles and histopathological classification, and it might help in early medical diagnosis, allowing automatic disease classification without the need of a human expert. In this paper we classify biomedical images using ensembles of neural networks. We create this ensemble using a ResNet50 architecture and modifying its activation layers by substituting ReLUs with other functions. We select our activations among the following ones: ReLU, leaky ReLU, Parametric ReLU, ELU, Adaptive Piecewice Linear Unit, S-Shaped ReLU, Swish , Mish, Mexican Linear Unit, Gaussian Linear Unit, Parametric Deformable Linear Unit, Soft Root Sign (SRS) and others. As a baseline, we used an ensemble of neural networks that only use ReLU activations. We tested our networks on several small and medium sized biomedical image datasets. Our results prove that our best ensemble obtains a better performance than the ones of the naive approaches. In order to encourage the reproducibility of this work, the MATLAB code of all the experiments will be shared at https://github.com/LorisNanni.
