  Deep learning methods have been successful in solving tasks in machine
learning and have made breakthroughs in many sectors owing to their ability to
automatically extract features from unstructured data. However, their
performance relies on manual trial-and-error processes for selecting an
appropriate network architecture, hyperparameters for training, and
pre-/post-procedures. Even though it has been shown that network architecture
plays a critical role in learning feature representation feature from data and
the final performance, searching for the best network architecture is
computationally intensive and heavily relies on researchers' experience.
Automated machine learning (AutoML) and its advanced techniques i.e. Neural
Architecture Search (NAS) have been promoted to address those limitations. Not
only in general computer vision tasks, but NAS has also motivated various
applications in multiple areas including medical imaging. In medical imaging,
NAS has significant progress in improving the accuracy of image classification,
segmentation, reconstruction, and more. However, NAS requires the availability
of large annotated data, considerable computation resources, and pre-defined
tasks. To address such limitations, meta-learning has been adopted in the
scenarios of few-shot learning and multiple tasks. In this book chapter, we
first present a brief review of NAS by discussing well-known approaches in
search space, search strategy, and evaluation strategy. We then introduce
various NAS approaches in medical imaging with different applications such as
classification, segmentation, detection, reconstruction, etc. Meta-learning in
NAS for few-shot learning and multiple tasks is then explained. Finally, we
describe several open problems in NAS.
