Semantic segmentation of various tissue and nuclei types in histology images is fundamental to many downstream tasks in the area of computational pathology (CPath). In recent years, Deep Learning (DL) methods have been shown to perform well on segmentation tasks but DL methods generally require a large amount of pixel-wise annotated data. Pixel-wise annotation sometimes requires expert's knowledge and time which is laborious and costly to obtain. In this paper, we present a consistency based semi-supervised learning (SSL) approach that can help mitigate this challenge by exploiting a large amount of unlabelled data for model training thus alleviating the need for a large annotated dataset. However, SSL models might also be susceptible to changing context and features perturbations exhibiting poor generalisation due to the limited training data. We propose an SSL method that learns robust features from both labelled and unlabelled images by enforcing consistency against varying contexts and feature perturbations. The proposed method incorporates context-aware consistency by contrasting pairs of overlapping images in a pixel-wise manner from changing contexts resulting in robust and context invariant features. We show that cross-consistency training makes the encoder features invariant to different perturbations and improves the prediction confidence. Finally, entropy minimisation is employed to further boost the confidence of the final prediction maps from unlabelled data. We conduct an extensive set of experiments on two publicly available large datasets (BCSS and MoNuSeg) and show superior performance compared to the state-of-the-art methods.

翻译：组织学图像中各类组织与细胞核的语义分割是计算病理学领域许多下游任务的基础。近年来，深度学习方法在分割任务上表现出色，但通常需要大量像素级标注数据。像素级标注有时需要专家知识和时间，获取过程费时费力且成本高昂。本文提出一种基于一致性的半监督学习方法，通过利用大量无标注数据进行模型训练来缓解这一挑战，从而减少对大规模标注数据集的需求。然而，半监督模型也可能因上下文变化和特征扰动而受到影响，由于训练数据有限，其泛化能力较差。我们提出一种半监督学习方法，通过强制在变化的上下文和特征扰动下保持一致性，从标注和无标注图像中学习鲁棒特征。该方法通过从变化上下文中以像素方式对比重叠图像对来引入上下文感知一致性，从而获得鲁棒且上下文不变的特征。我们证明交叉一致性训练能使编码器特征对不同的扰动具有不变性，并提升预测置信度。最后，采用熵最小化进一步增强无标注数据最终预测图的置信度。我们在两个公开大型数据集（BCSS和MoNuSeg）上进行了广泛实验，结果表明该方法相比现有最先进方法具有更优性能。