Abstract
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.
Abstract (translated)
在病理学图像中的语义分割各种组织和核型类型是计算病理学(CPath)领域许多后续任务的基础。近年来,深度学习(DL)方法已经表明在分割任务方面表现良好,但DL方法通常需要大量像素级注释数据。像素级注释有时需要专家知识和时间,这是繁琐的并且昂贵的。在本文中,我们提出了一种基于一致性的半监督学习(SSL)方法,可以帮助缓解这种挑战,通过利用未标记数据进行模型训练,从而缓解需要大型注释数据的需求。然而,SSL模型可能也受到上下文和特征干扰的影响,由于训练数据有限,表现出 poor generalization。我们提出了一种SSL方法,可以从标记和未标记图像中学习 robust features,并通过 enforcement consistency 对抗不同上下文和特征干扰。该方法将Context-aware consistency融入其中,通过比较改变上下文的一对重叠图像的像素级方式,实现 robust 和上下文不变的特征。我们表明,交叉一致性训练使编码器特征对不同干扰适应不变,并提高预测信心。最后,熵最小化被使用来提高未标记数据最终预测映射的信心。我们利用两个公开可用的大型数据集(BCSS和MoNuSeg)进行了广泛的实验,并表现出与当前方法相比更好的性能。
URL
https://arxiv.org/abs/2301.13141