Unsupervised Domain Adaptation for Medical Images with an Improved Combination of Losses

Ravi Kant Gupta, Shounak Das, Amit Sethi

Abstract: This paper presents a novel approach for unsupervised domain adaptation that is tested on H&E stained histology and retinal fundus images. Existing adversarial domain adaptation methods may not effectively align different domains of multimodal distributions associated with classification problems. Since our objective is to enhance domain alignment and reduce domain shifts between these domains by leveraging their unique characteristics, we propose a tailored loss function to address the challenges specific to medical 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 texture-specific features, such as tissue structure and cell morphology, to enhance adaptation performance in the histology domain. The proposed method – Domain Adaptive Learning (DAL) – was extensively evaluated for accuracy, robustness, and generalization. We conducted experiments on the FHIST and a retina data