Addressing Annotation Scarcity in Hyperspectral Brain Image Segmentation with Unsupervised Domain Adaptation
This addresses annotation scarcity for biomedical imaging researchers, offering a domain-specific solution that is incremental in applying domain adaptation to this niche.
The paper tackles the problem of severe label scarcity in segmenting cerebral vasculature from hyperspectral brain images by using unsupervised domain adaptation, achieving significant performance improvements over existing state-of-the-art methods.
This work presents a novel deep learning framework for segmenting cerebral vasculature in hyperspectral brain images. We address the critical challenge of severe label scarcity, which impedes conventional supervised training. Our approach utilizes a novel unsupervised domain adaptation methodology, using a small, expert-annotated ground truth alongside unlabeled data. Quantitative and qualitative evaluations confirm that our method significantly outperforms existing state-of-the-art approaches, demonstrating the efficacy of domain adaptation for label-scarce biomedical imaging tasks.