English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Deep learning methods have been successful in solving tasks in machinelearning and have made breakthroughs in many sectors owing to their ability toautomatically extract features from unstructured data. However, theirperformance relies on manual trial-and-error processes for selecting anappropriate network architecture, hyperparameters for training, andpre-/post-procedures. Even though it has been shown that network architectureplays a critical role in learning feature representation feature from data andthe final performance, searching for the best network architecture iscomputationally intensive and heavily relies on researchers' experience.Automated machine learning (AutoML) and its advanced techniques i.e. NeuralArchitecture Search (NAS) have been promoted to address those limitations. Notonly in general computer vision tasks, but NAS has also motivated variousapplications 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 availabilityof large annotated data, considerable computation resources, and pre-definedtasks. To address such limitations, meta-learning has been adopted in thescenarios of few-shot learning and multiple tasks. In this book chapter, wefirst present a brief review of NAS by discussing well-known approaches insearch space, search strategy, and evaluation strategy. We then introducevarious NAS approaches in medical imaging with different applications such asclassification, segmentation, detection, reconstruction, etc. Meta-learning inNAS for few-shot learning and multiple tasks is then explained. Finally, wedescribe several open problems in NAS.

Meta-Learning of NAS for Few-shot Learning in Medical Image Applications