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.

这篇论文提出了一种针对H&E染色组织学图像的无监督领域自适应(UDA)的新方法，通过利用组织结构和细胞形态等组织学特征来提高领域适应性能。该方法在准确性、鲁棒性和泛化性方面经过了广泛评估，并且在FHIST数据集上的实验结果表明，我们提出的方法(Domain Adaptive Learning，DAL)明显超过了基于ViT和CNN的最先进方法分别有1.41%和6.56%的提升。

领域适应学习: 改进损失函数组合的组织学图像无监督适应