Distant Domain Transfer Learning for Medical Image Classification: A Review

Lack of sufficient labeled data and misalignment between domain distributions are two major challenges in medical image classification. While deep learning has demonstrated remarkable success in addressing these issues through its feature extraction capabilities and domain adaptation potential, it still requires large amounts of labeled data and is often sensitive to domain shifts. However, collecting extensive labeled datasets in the medical field is challenging due to privacy policies and limited patient samples. Distant Domain Transfer Learning (DDTL) offers a novel solution by enabling knowledge transfer across unrelated domains while mitigating negative transfer caused by data distribution mismatch. This review systematically explores state-of-the-art DDTL methodologies, including feature-based adaptation, domaininvariant representation learning, and adversarial domain adaptation. Furthermore, we analyze the role of deep feature extraction models such as ResNet and EfficientNet in minimizing domain shift. A comparative evaluation of existing approaches across various medical imaging datasets is presented, emphasizing their effectiveness, limitations, and future research directions. By synthesizing insights from recent literature, this review provides a comprehensive understanding of DDTL's potential in advancing robust and generalizable medical image classification systems.