Search papers, labs, and topics across Lattice.
School of Computer Science and Technology, Tongji University, Shanghai, 201804, China
3
0
5
0
Achieving precise medical image segmentation with limited labeled data is now possible by embracing intra-class heterogeneity through innovative prototype learning.
VLM agents exhibit vastly different skill evolution patterns, revealing that initial performance scores can be misleading without considering improvement dynamics.
Overcome the limitations of existing semi-supervised segmentation methods by learning structural consensus across samples, achieving more generalizable pancreas segmentation under sparse supervision.
