Samuel Arthur Ameyaw

This review provides a comprehensive metasynthesis of contemporary algorithmic mitigation paradigms designed to alleviate distribution discrepancies, evaluating techniques ranging from classical stain normalization and adversarial feature alignment to test-time adaptation and large-scale self-supervised pathology foundation models.