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.

翻译：在本文中,我们提出了一个解决生物医学应用的半监督分类任务的新方法,涉及一个受监督的自动编码网络。我们创建了一个将标签编码成自动编码器潜在空间的网络结构,并定义了将分类和重建损失相结合的全球标准。我们用双下推算法对半监督自动编码器(SAE)进行了标签数据培训。然后,我们用一个软式马克分级器对知识网络使用无标签样本进行分类,用于提供每类分类的分类信任分数的潜在空间。我们用PyTorch框架对模型、优化器、排程器和损失功能采用了我们的SAEA方法。我们将我们的半监督自动编码法(SAE)与典型的半监督方法,如Label Propagation和拉贝尔传播,以及一个完全连接的神经网络(FCNN)进行了比较。实验显示,SAE在合成数据集和两个真实生物数据集上,SASSE优于Label Propagation和扩展以及完全连接的神经网络。