Detecting cerebral microbleeds via deep learning with features enhancement by reusing ground truth.

Journal: Computer methods and programs in biomedicine
Published Date: Mar 12, 2021

Abstract

BACKGROUND AND OBJECTIVES: Cerebral microbleeds (CMBs) are cerebral small vascular diseases and are often used to diagnose symptoms such as stroke and dementia. Manual detection of cerebral microbleeds is a time-consuming and error-prone task, so the application of microbleed detection algorithms based on deep learning is of great significance. This study presents the feature enhancement technology applying to improve the performances of detecting CMBs. The primary purpose of the feature enhancement is emphasizing the meaningful features, leading deep learning network easier and correctly to optimize.

Authors

  • Tianfu Li
    Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China.
  • Yan Zou
    National Clinical Research Center of Oral Diseases, Shanghai 200011, China.
  • Pengfei Bai
    Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China; National Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou 510006, China. Electronic address: baipf@scnu.edu.cn.
  • Shixiao Li
    Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China.
  • Huawei Wang
    Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China.
  • Xingliang Chen
    Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China.
  • Zhanao Meng
    Department of Radiology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Zhuang Kang
    Department of Radiology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Guofu Zhou
    Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China; National Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou 510006, China; Academy of Shenzhen Guohua Optoelectronics, Shenzhen 518110, China.

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