KaryoXpert: An accurate chromosome segmentation and classification framework for karyotyping analysis without training with manually labeled metaphase-image mask annotations

Siyuan Chen, Kaichuang Zhang, Jingdong Hu, Na Li, Ao Xu, Haoyang Li, Juexiao Zhou, Chao Huang, Yongguo Yu, Xin Gao

Abstract: Highlights•KaryoXpert can be trained without manually annotated ground truth instance masks.•It merges advantages from both morphology algorithms and deep-learning models.•It performs high accuracy despite domain shifts, batch effects, and class similarity.•GPU acceleration enables real-time inference with cutting-edge clinical accuracy.