Domain-Adaptive Learning: Unsupervised Adaptation for Histology Images
  with Improved Loss Function Combination
Ravi Kant Gupta Shounak Das Amit Sethi
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.