Login / Signup

[The Latest Trends in Attention Mechanisms and Their Application in Medical Imaging].

Hyungseob ShinJeongryong LeeTaejoon EoYohan JunSewon KimDosik Hwang
Published in: Taehan Yongsang Uihakhoe chi (2020)
Deep learning has recently achieved remarkable results in the field of medical imaging. However, as a deep learning network becomes deeper to improve its performance, it becomes more difficult to interpret the processes within. This can especially be a critical problem in medical fields where diagnostic decisions are directly related to a patient's survival. In order to solve this, explainable artificial intelligence techniques are being widely studied, and an attention mechanism was developed as part of this approach. In this paper, attention techniques are divided into two types: post hoc attention, which aims to analyze a network that has already been trained, and trainable attention, which further improves network performance. Detailed comparisons of each method, examples of applications in medical imaging, and future perspectives will be covered.
Keyphrases
  • deep learning
  • artificial intelligence
  • working memory
  • healthcare
  • high resolution
  • machine learning
  • big data
  • case report
  • resistance training
  • photodynamic therapy
  • network analysis
  • high intensity
  • drug induced