Method to Minimize the Errors of AI: Quantifying and Exploiting Uncertainty of Deep Learning in Brain Tumor Segmentation.

Journal: Sensors (Basel, Switzerland)
Published Date: Mar 21, 2022

Abstract

Despite the unprecedented success of deep learning in various fields, it has been recognized that clinical diagnosis requires extra caution when applying recent deep learning techniques because false prediction can result in severe consequences. In this study, we proposed a reliable deep learning framework that could minimize incorrect segmentation by quantifying and exploiting uncertainty measures. The proposed framework demonstrated the effectiveness of a public dataset: Multimodal Brain Tumor Segmentation Challenge 2018. By using this framework, segmentation performances, particularly for small lesions, were improved. Since the segmentation of small lesions is difficult but also clinically significant, this framework could be effectively applied to the medical imaging field.

Authors

  • Joohyun Lee
    Department of Electrical and Electronic Engineering, Hanyang University, Ansan 15588, Korea.
  • Dongmyung Shin
    Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea.
  • Se-Hong Oh
    Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea.
  • Haejin Kim
    College of Science & Technology, Hongik University, Sejong 30016, Korea.

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