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Abstract
In recent years, transfer learning has emerged as a pivotal strategy to address data scarcity in medical imaging, especially in low-resource settings where labeled diagnostic images are limited. This paper explores cross-domain transfer learning techniques that leverage pretrained models from high-resource domains to improve diagnostic performance in underrepresented contexts. We evaluate existing literature, propose an integrated framework for cross-domain adaptation, and provide comparative results across different architectures. Our findings suggest that fine-tuning on augmented datasets combined with domain adaptation significantly enhances model generalization in low-resource environments.
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