In recent years, deep learning as a state-of-the-art machine learning technique has made great success in histopathological image classification. However, most of deep learning approaches rely heavily on the substantial task-specific annotations, which require experienced pathologists’ manual labelling. As a result, they are laborious and time-consuming, and many unlabeled pathological images are difficult to use without experts’ annotations. To mitigate the requirement for data annotation, we propose a self-supervised Deep Adaptive Regularized Clustering (DARC) framework to pre-train a neural network. DARC iteratively clusters the learned representations and utilizes the cluster assignments as pseudo-labels to learn the parameters of the network. To learn feasible representations and encourage the representations to become more discriminative, we design an objective function combining a network loss with a clustering loss using an adaptive regularization function, which is updated adaptively throughout the training process to learn feasible representations. The proposed DARC is evaluated on three public datasets, including NCT-CRC-HE-100K, PCam and LC25000. Compared to the strategy of training from scratch, fine-tuning using the pre-trained weights of DARC can obviously boost the accuracy of neural networks on histopathological classification. The accuracy of using the network trained using DARC pre-trained weights with only 10% labeled data is already comparable to the network trained from scratch with 100% training data. The network using DARC pre-trained weights achieves the fastest convergence speed on the downstream classification task. Moreover, visualization through t-distributed stochastic neighbor embedding (t-SNE) shows that the learned representations are generalizable and discriminative.

近年来，深度学习作为最先进的机器学习技术在组织病理学图像分类方面取得了巨大成功。然而，大多数深度学习方法严重依赖大量特定于任务的注释，这需要经验丰富的病理学家手动标记。因此，它们既费力又耗时，许多未标记的病理图像在没有专家注释的情况下难以使用。为了减轻对数据注释的要求，我们提出了一种自我监督的深度自适应正则化聚类 (DARC) 框架来预训练神经网络。DARC 迭代地对学习的表示进行聚类，并利用聚类分配作为伪标签来学习网络的参数。为了学习可行的表示并鼓励表示变得更具辨别力，我们使用自适应正则化函数设计了一个将网络损失与聚类损失相结合的目标函数，该函数在整个训练过程中自适应更新以学习可行的表示。提议的 DARC 在三个公共数据集上进行评估，包括 NCT-CRC-HE-100K、PCam 和 LC25000。与从头开始训练的策略相比，使用 DARC 的预训练权重进行微调可以明显提高神经网络在组织病理学分类上的准确性。使用仅使用 10% 标记数据的 DARC 预训练权重训练的网络的准确性已经与使用 100% 训练数据从头训练的网络相媲美。使用 DARC 预训练权重的网络在下游分类任务上实现了最快的收敛速度。而且，