  Pixel-level dense labeling is both resource-intensive and time-consuming,
whereas weak labels such as scribble present a more feasible alternative to
full annotations. However, training segmentation networks with weak supervision
from scribbles remains challenging. Inspired by the fact that different
segmentation tasks can be correlated with each other, we introduce a new
approach to few-scribble supervised segmentation based on model parameter
interpolation, termed as ModelMix. Leveraging the prior knowledge that linearly
interpolating convolution kernels and bias terms should result in linear
interpolations of the corresponding feature vectors, ModelMix constructs
virtual models using convex combinations of convolutional parameters from
separate encoders. We then regularize the model set to minimize vicinal risk
across tasks in both unsupervised and scribble-supervised way. Validated on
three open datasets, i.e., ACDC, MSCMRseg, and MyoPS, our few-scribble guided
ModelMix significantly surpasses the performance of the state-of-the-art
scribble supervised methods.
