Cooperative GAN: Automated tympanic membrane anomaly detection using a Cooperative Observation Network.

Journal: Computer methods and programs in biomedicine
PMID:

Abstract

BACKGROUND AND OBJECTIVES: Recently, artificial intelligence (AI) has been applied to otolaryngology. However, existing supervised learning methods cannot easily predict data outside the learning domain. Moreover, collecting diverse medical data has become demanding owing to privacy concerns. Consequently, these limitations hinder the applications of AI in clinical settings. To address these issues, this study proposes a Cooperative Observation Network (CON), using an unsupervised anomaly detection approach. Anomaly detection is the process of identifying data patterns that deviate from the majority.

Authors

  • Dahye Song
    Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University ERICA, Ansan 15208, Republic of Korea. Electronic address: thdekgp99@hanyang.ac.kr.
  • Younghan Chung
    Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan 15208, Republic of Korea. Electronic address: hanichung95@gmail.com.
  • Jaeyoung Kim
    School of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan, South Korea m.m.manjurul@gmail.com, kjy7079@gmail.com, sherazalik@gmail.com, jongmyon.kim@gmail.com.
  • June Choi
    Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Republic of Korea.
  • Yeonjoon Lee
    Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University ERICA, Ansan 15208, Republic of Korea. Electronic address: yeonjoonlee@hanyang.ac.kr.