We propose an unsupervised method using self-clustering convolutional adversarial autoencoders to classify prostate tissue as tumor or non-tumor without any labeled training data. The clustering method is integrated into the training of the autoencoder and requires only little post-processing. Our network trains on hematoxylin and eosin (H&E) input patches and we tested two different reconstruction targets, H&E and immunohistochemistry (IHC). We show that antibody-driven feature learning using IHC helps the network to learn relevant features for the clustering task. Our network achieves a F1 score of 0.62 using only a small set of validation labels to assign classes to clusters.

翻译：我们建议采用一种不受监督的方法,使用自我集群组合式对抗式自动算法,将前列腺组织分类为肿瘤或非扰动,而没有任何标记的培训数据。集群方法被纳入自动编码器的培训中,只需要很少的加工后使用。我们的网络用血氧素和环辛(H&E)输入补丁进行火车,我们测试了两个不同的重建目标,即H&E和免疫物理化学(IHC)。我们显示,使用IHC进行的抗体驱动特征学习有助于网络学习集群任务的相关特征。我们的网络只用一套小的验证标签来分配集群课程,而获得0.62的F1分。