Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells

arXiv:2401.07278v3 Announce Type: replace
Abstract: Artificial Intelligence (AI) in healthcare, especially in white blood cell cancer diagnosis, is hindered by two primary challenges: the lack of large-scale labeled datasets for white blood cell (WBC) segmentation and outdated segmentation methods. These challenges inhibit the development of more accurate and modern techniques to diagnose cancer relating to white blood cells. To address the first challenge, a semi-supervised learning framework should be devised to efficiently capitalize on the scarcity of the dataset available. …

abstract artificial artificial intelligence arxiv cancer cancer diagnosis cells challenges cs.ai cs.cv datasets development diagnosis healthcare intelligence modern scale segmentation self-training semantic semi-supervised training type