  Deep learning based medical image segmentation models usually require large
datasets with high-quality dense segmentations to train, which are very
time-consuming and expensive to prepare. One way to tackle this challenge is by
using the mixed-supervised learning framework, in which only a part of data is
densely annotated with segmentation label and the rest is weakly labeled with
bounding boxes. The model is trained jointly in a multi-task learning setting.
In this paper, we propose Mixed-Supervised Dual-Network (MSDN), a novel
architecture which consists of two separate networks for the detection and
segmentation tasks respectively, and a series of connection modules between the
layers of the two networks. These connection modules are used to transfer
useful information from the auxiliary detection task to help the segmentation
task. We propose to use a recent technique called "Squeeze and Excitation" in
the connection module to boost the transfer. We conduct experiments on two
medical image segmentation datasets. The proposed MSDN model outperforms
multiple baselines.
