Meta multi-task nuclei segmentation with fewer training samples

Chu Han, Huasheng Yao, Bingchao Zhao, Zhenhui Li, Zhenwei Shi, Lei Wu, Xin Chen, Jinrong Qu, Ke Zhao, Rushi Lan, Changhong Liang, Xipeng Pan, Zaiyi Liu

Abstract: Highlights•We proposed a meta multi-task learning scheme (Meta-MTL) for nuclei segmentation with better domain adaptation and less data dependency.•In the contour-aware multi-task learning model, we proposed a feature fusion and interaction block (FFIB) for features communication between two parallel tasks.•Our proposed model achieves comparable performances with state-of-the-art models with fewer training samples. And the proposed model performs well on domain adaptation and domain generalization.