Uncertainty-guided annotation enhances segmentation with the human-in-the-loop

arXiv:2404.07208v1 Announce Type: new
Abstract: Deep learning algorithms, often critiqued for their 'black box' nature, traditionally fall short in providing the necessary transparency for trusted clinical use. This challenge is particularly evident when such models are deployed in local hospitals, encountering out-of-domain distributions due to varying imaging techniques and patient-specific pathologies. Yet, this limitation offers a unique avenue for continual learning. The Uncertainty-Guided Annotation (UGA) framework introduces a human-in-the-loop approach, enabling AI to convey its uncertainties to clinicians, effectively acting …

abstract algorithms annotation arxiv black box box challenge clinical cs.ai cs.cv cs.hc deep learning deep learning algorithms domain hospitals human imaging loop nature patient segmentation transparency type uncertainty