论文标题
使用监督的自动编码器进行生物医学应用的半监督分类
Semi-supervised classification using a supervised autoencoder for biomedical applications
论文作者
论文摘要
在本文中，我们提出了一种新的方法，用于解决涉及监督自动编码网络的生物医学应用程序的半监督分类任务。我们创建了一个网络体系结构，该网络体系结构将标签编码为自动编码器的潜在空间，并定义结合分类和重建损失的全局标准。我们使用双重下降算法在标记的数据上训练半监督自动编码器（SSAE）。然后，我们使用博学的网络对未标记的样本进行了分类，这要归功于应用于潜在空间的SoftMax分类器，该空间为每个类提供了分类置信度得分。 我们使用模型，优化器，调度程序和损失功能的Pytorch框架实现了SSAE方法。我们将半监督自动编码器方法（SSAE）与经典的半监督方法（例如标签传播和标签扩展）以及完全连接的神经网络（FCNN）进行了比较。实验表明，SSAE在合成数据集和两个现实世界中的生物数据集上都超过标签传播和扩散以及完全连接的神经网络。
In this paper we present a new approach to solve semi-supervised classification tasks for biomedical applications, involving a supervised autoencoder network. We create a network architecture that encodes labels into the latent space of an autoencoder, and define a global criterion combining classification and reconstruction losses. We train the Semi-Supervised AutoEncoder (SSAE) on labelled data using a double descent algorithm. Then, we classify unlabelled samples using the learned network thanks to a softmax classifier applied to the latent space which provides a classification confidence score for each class. We implemented our SSAE method using the PyTorch framework for the model, optimizer, schedulers, and loss functions. We compare our semi-supervised autoencoder method (SSAE) with classical semi-supervised methods such as Label Propagation and Label Spreading, and with a Fully Connected Neural Network (FCNN). Experiments show that the SSAE outperforms Label Propagation and Spreading and the Fully Connected Neural Network both on a synthetic dataset and on two real-world biological datasets.
