  Accurate medical image segmentation especially for echocardiographic images
with unmissable noise requires elaborate network design. Compared with manual
design, Neural Architecture Search (NAS) realizes better segmentation results
due to larger search space and automatic optimization, but most of the existing
methods are weak in layer-wise feature aggregation and adopt a ``strong
encoder, weak decoder" structure, insufficient to handle global relationships
and local details. To resolve these issues, we propose a novel semi-supervised
hybrid NAS network for accurate medical image segmentation termed SSHNN. In
SSHNN, we creatively use convolution operation in layer-wise feature fusion
instead of normalized scalars to avoid losing details, making NAS a stronger
encoder. Moreover, Transformers are introduced for the compensation of global
context and U-shaped decoder is designed to efficiently connect global context
with local features. Specifically, we implement a semi-supervised algorithm
Mean-Teacher to overcome the limited volume problem of labeled medical image
dataset. Extensive experiments on CAMUS echocardiography dataset demonstrate
that SSHNN outperforms state-of-the-art approaches and realizes accurate
segmentation. Code will be made publicly available.
