  Different from handcrafted features, deep neural networks can automatically
learn task-specific features from data. Due to this data-driven nature, they
have achieved remarkable success in various areas. However, manual design and
selection of suitable network architectures are time-consuming and require
substantial effort of human experts. To address this problem, researchers have
proposed neural architecture search (NAS) algorithms which can automatically
generate network architectures but suffer from heavy computational cost and
instability if searching from scratch. In this paper, we propose a hybrid NAS
framework for ultrasound (US) image classification and segmentation. The hybrid
framework consists of a pre-trained backbone and several searched cells (i.e.,
network building blocks), which takes advantage of the strengths of both NAS
and the expert knowledge from existing convolutional neural networks.
Specifically, two effective and lightweight operations, a mixed depth-wise
convolution operator and a squeeze-and-excitation block, are introduced into
the candidate operations to enhance the variety and capacity of the searched
cells. These two operations not only decrease model parameters but also boost
network performance. Moreover, we propose a re-aggregation strategy for the
searched cells, aiming to further improve the performance for different vision
tasks. We tested our method on two large US image datasets, including a 9-class
echinococcosis dataset containing 9566 images for classification and an ovary
dataset containing 3204 images for segmentation. Ablation experiments and
comparison with other handcrafted or automatically searched architectures
demonstrate that our method can generate more powerful and lightweight models
for the above US image classification and segmentation tasks.
