AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 9, 2019

Abstract

BACKGROUND AND OBJECTIVES: The multi-label Human Protein Atlas (HPA) classification can yield a better understanding of human diseases and help doctors to enhance the automatic analysis of biomedical images. The existing automatic protein recognition methods have been limited to single pattern. Therefore, an automatic multi-label human protein atlas recognition system with satisfactory performance should be conducted. This work aims to build an automatic recognition system for multi-label human protein atlas classification based on deep learning.

Authors

  • Shao Xiang
    College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Hunan University, Changsha 410082, China; National Engineering Laboratory for Robot Vision Perception and Control technologies, Hunan University, Changsha 410082, China.
  • Qiaokang Liang
  • Yucheng Hu
    College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Hunan University, Changsha 410082, China; National Engineering Laboratory for Robot Vision Perception and Control technologies, Hunan University, Changsha 410082, China.
  • Pen Tang
    College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Hunan University, Changsha 410082, China; National Engineering Laboratory for Robot Vision Perception and Control technologies, Hunan University, Changsha 410082, China.
  • Gianmarc Coppola
  • Dan Zhang
    School of Pharmacy, Southwest Medical University, Luzhou 646000, China.
  • Wei Sun
    Sutra Medical Inc, Lake Forest, CA.

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