Adaptive Segmentation

Adaptive segmentation focuses on developing image segmentation methods robust to variations in data distribution, addressing challenges like limited labeled data in target domains or differences in image acquisition settings. Current research emphasizes unsupervised domain adaptation techniques, often employing self-supervised learning, hypernetworks to handle varying image resolutions, and classification-based routing to specialized segmentation models. These advancements improve the accuracy and efficiency of segmentation across diverse datasets and applications, particularly impacting medical image analysis and scene understanding where data heterogeneity is common.