Neural architecture search for medical image applications

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/postprocedures. Even it has been proven that network architecture plays a critical role to the feature representation of data and the final performance, design of the network architecture is computationally intensive and heavily relies 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, NAS has motivated various applications in multiple areas including medical imaging. In medical imaging, NAS has a significant progress in improving the accuracy of image classification, segmentation, reconstruction and more. In this book chapter, we first revise the background of NAS by providing documents of 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. Finally, we describe several open problems in NAS.