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This paper presents a novel approach for unsupervised domain adaptation (UDA)targeting H&E stained histology images. Existing adversarial domain adaptationmethods may not effectively align different domains of multimodal distributionsassociated with classification problems. The objective is to enhance domainalignment and reduce domain shifts between these domains by leveraging theirunique characteristics. Our approach proposes a novel loss function along withcarefully selected existing loss functions tailored to address the challengesspecific to histology images. This loss combination not only makes the modelaccurate and robust but also faster in terms of training convergence. Wespecifically focus on leveraging histology-specific features, such as tissuestructure and cell morphology, to enhance adaptation performance in thehistology domain. The proposed method is extensively evaluated in accuracy,robustness, and generalization, surpassing state-of-the-art techniques forhistology images. We conducted extensive experiments on the FHIST dataset andthe results show that our proposed method - Domain Adaptive Learning (DAL)significantly surpasses the ViT-based and CNN-based SoTA methods by 1.41% and6.56% respectively.

Domain-Adaptive Learning: Unsupervised Adaptation for Histology Images with Improved Loss Function Combination