This paper presents a novel approach for unsupervised domain adaptation (UDA) targeting H&E stained histology images. Existing adversarial domain adaptation methods may not effectively align different domains of multimodal distributions associated with classification problems. The objective is to enhance domain alignment and reduce domain shifts between these domains by leveraging their unique characteristics. Our approach proposes a novel loss function along with carefully selected existing loss functions tailored to address the challenges specific to histology images. This loss combination not only makes the model accurate and robust but also faster in terms of training convergence. We specifically focus on leveraging histology-specific features, such as tissue structure and cell morphology, to enhance adaptation performance in the histology domain. The proposed method is extensively evaluated in accuracy, robustness, and generalization, surpassing state-of-the-art techniques for histology images. We conducted extensive experiments on the FHIST dataset and the results show that our proposed method - Domain Adaptive Learning (DAL) significantly surpasses the ViT-based and CNN-based SoTA methods by 1.41% and 6.56% respectively.

翻译：本文提出了一种针对H&E染色组织学图像的无监督领域自适应（UDA）新方法。现有对抗性领域自适应方法可能无法有效对齐与分类问题相关的多模态分布的不同领域。本文旨在通过利用各领域的独特特征，增强领域对齐并减少领域偏移。该方法在精心筛选现有损失函数的基础上，提出了一种专为组织学图像挑战设计的新型损失函数。该损失组合不仅提升了模型的准确性和鲁棒性，还加快了训练收敛速度。我们特别关注利用组织学特异性特征（如组织结构与细胞形态），以增强组织学领域的自适应性能。所提方法在准确性、鲁棒性和泛化性方面进行了全面评估，在组织学图像上超越了现有最优技术。我们在FHIST数据集上的大量实验表明，我们提出的领域自适应学习（DAL）方法分别以1.41%和6.56%的显著优势超越了基于ViT和基于CNN的最优方法。