  Despite recent progress on semantic segmentation, there still exist huge
challenges in medical ultra-resolution image segmentation. The methods based on
multi-branch structure can make a good balance between computational burdens
and segmentation accuracy. However, the fusion structure in these methods
require to be designed elaborately to achieve desirable result, which leads to
model redundancy. In this paper, we propose Meta Segmentation Network (MSN) to
solve this challenging problem. With the help of meta-learning, the fusion
module of MSN is quite simple but effective. MSN can fast generate the weights
of fusion layers through a simple meta-learner, requiring only a few training
samples and epochs to converge. In addition, to avoid learning all branches
from scratch, we further introduce a particular weight sharing mechanism to
realize a fast knowledge adaptation and share the weights among multiple
branches, resulting in the performance improvement and significant parameters
reduction. The experimental results on two challenging ultra-resolution medical
datasets BACH and ISIC show that MSN achieves the best performance compared
with the state-of-the-art methods.
