Patient stratification has made a great contribution to efficient and personalized medicine. An important task in patient stratification is to discover quite distinct disease subtypes for effective treatment. In this paper, we propose a new deep learning and clustering model which combines Deep Belief Network (DBN) and Fuzzy C-Means(FCM), called Unsupervised Deep Fuzzy C-Means clustering Network(UDFCMN), to cluster lung cancer patients from lung CT images. In our deep clustering network, images after preprocessing are first encoded into multiple layers of hidden variables to extract hierarchical features and feature distribution and form the high-level representations. Here, to solve the problem of feature homogenization in DBN, we introduce the Winner-Take-All (WTA) idea to meliorate the traditional DBN structure, called WTADBN. Then FCM is used to produce the initial cluster labels with the new representations learnt by stacked WTARBM. Therefore, the FCM-generated cluster labels are used for the fine-tuning of the DBN as ground-truth labels. And an unsupervised image clustering and patient stratification process is completed by cross iteration. We tested our deep FCM clustering algorithm to do experiment on both public dataset from the internet and private dataset from cooperate hospital. For the latter one, the clinical and biological verification was also performed. Experimental results reveal outperformance of UDFCMN as compared to the state-of-the-art unsupervised classification methods. These results also indicate that our approach may have practical applications in lung cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy.

患者分层为高效和个性化医疗做出了巨大贡献。患者分层的一项重要任务是发现有效治疗的截然不同的疾病亚型。在本文中，我们提出了一种新的深度学习和聚类模型，该模型将深度信念网络（DBN）和模糊C均值（FCM）结合在一起，称为无监督深度模糊C均值聚类网络（UDFCMN），以对肺癌的肺癌患者进行聚类CT图像。在我们的深度聚类网络中，预处理后的图像首先被编码为多层隐藏变量，以提取层次特征和特征分布并形成高级表示。在这里，为了解决DBN中的特征同化问题，我们引入了Winner-Take-All（WTA）思想，以改善传统的DBN结构WTADBN。然后，使用FCM生成初始簇标签，并使用堆叠式WTARBM学习的新表示形式。因此，FCM生成的簇标签用于对DBN进行微调，作为地面真实标签。通过交叉迭代完成了无监督的图像聚类和患者分层过程。我们测试了我们的深度FCM聚类算法，以对互联网上的公共数据集和合作医院的私有数据集进行实验。对于后者，还进行了临床和生物学验证。实验结果表明，与最新的无监督分类方法相比，UDFCMN的性能更高。这些结果还表明，我们的方法可能在肺癌发病机理研究中具有实际应用，并为个性化癌症治疗提供有用的指导。