Unsupervised domain adaptation (UDA) methods have been broadly utilized to improve the models' adaptation ability in general computer vision. However, different from the natural images, there exist huge semantic gaps for the nuclei from different categories in histopathology images. It is still under-explored how could we build generalized UDA models for precise segmentation or classification of nuclei instances across different datasets. In this work, we propose a novel deep neural network, namely Category-Aware feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei instance segmentation and classification. Specifically, we first propose a category-level feature alignment module with dynamic learnable trade-off weights. Second, we propose to facilitate the model performance on the target data via self-supervised training with pseudo labels based on nuclei-level prototype features. Comprehensive experiments on cross-domain nuclei instance segmentation and classification tasks demonstrate that our approach outperforms state-of-the-art UDA methods with a remarkable margin.
翻译:未受监督的域适应方法(UDA)被广泛用于提高模型的一般计算机视觉适应能力,但与自然图像不同,在组织病理学图象的不同类别中,核心部分存在巨大的语义差距,目前仍在探索中,如何建立通用UDA模型,对不同数据集的核体进行精确分解或分类。在这项工作中,我们提议建立一个新型的深神经网络,即用于UDA核核体分解和分类的CAL-Net 和 Pseudo-Labelling 网络(CAPL-Net ) 。具体地说,我们首先提出一个具有动态可学习权重的分类特征调整模块。第二,我们提议通过以基于核级原型特征的假标签进行自我监督的培训,促进目标数据模型的性能。关于跨部核体样分解和分类任务的全面实验表明,我们的方法在显著的幅度上超越了UDA的状态方法。