Deep embeddings and logistic regression for rapid active learning in histopathological images.

Journal: Computer methods and programs in biomedicine
Published Date: Oct 13, 2021

Abstract

BACKGROUND AND OBJECTIVE: Recognizing different tissue components is one of the most fundamental and essential works in digital pathology. Current methods are often based on convolutional neural networks (CNNs), which need numerous annotated samples for training. Creating large-scale histopathological datasets is labor-intensive, where interactive data annotation is a potential solution.

Authors

  • Yiping Jiao
    Shool of Automation, Southeast University, 2nd Sipailou Road, Nanjing, China. Electronic address: ping@seu.edu.cn.
  • Jie Yuan
    Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, National Center of Technology Innovation for Synthetic Biology, No. 32, Xiqi Road, Tianjin Airport Economic Park, Tianjin 300308, China.
  • Yong Qiang
    School of Automation, Southeast University, 2nd Sipailou Road, Nanjing, China. Electronic address: qykingda@163.com.
  • Shumin Fei
    School of Automation, Southeast University, Nanjing, Jiangsu 210096, PR China.

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