Curriculum Knowledge Switching for Pancreas Segmentation
Yumou Tang Kun Zhan Zhibo Tian Mingxuan Zhang Saisai Wang Xueming Wen
Pancreas segmentation is challenging due to the small proportion and highly
changeable anatomical structure. It motivates us to propose a novel
segmentation framework, namely Curriculum Knowledge Switching (CKS) framework,
which decomposes detecting pancreas into three phases with different difficulty
extent: straightforward, difficult, and challenging. The framework switches
from straightforward to challenging phases and thereby gradually learns to
detect pancreas. In addition, we adopt the momentum update parameter updating
mechanism during switching, ensuring the loss converges gradually when the
input dataset changes. Experimental results show that different neural network
backbones with the CKS framework achieved state-of-the-art performance on the
NIH dataset as measured by the DSC metric.