Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation

  • Jae-Hyeok Lee
  • Seong Tae Kim
  • Hakmin Lee
  • Yong Man RoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11134)


This paper deals with a method for generating realistic labeled masses. Recently, there have been many attempts to apply deep learning to various bio-image computing fields including computer-aided detection and diagnosis. In order to learn deep network model to be well-behaved in bio-image computing fields, a lot of labeled data is required. However, in many bioimaging fields, the large-size of labeled dataset is scarcely available. Although a few researches have been dedicated to solving this problem through generative model, there are some problems as follows: (1) The generated bio-image does not seem realistic; (2) the variation of generated bio-image is limited; and (3) additional label annotation task is needed. In this study, we propose a realistic labeled bio-image generation method through visual feature processing in latent space. Experimental results have shown that mass images generated by the proposed method were realistic and had wide expression range of targeted mass characteristics.


Feature processing in latent space Image synthesis Bio-image generation Medical mass generation 



This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-01778, Development of Explainable Human-level Deep Machine Learning Inference Framework).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jae-Hyeok Lee
    • 1
  • Seong Tae Kim
    • 1
  • Hakmin Lee
    • 1
  • Yong Man Ro
    • 1
    Email author
  1. 1.School of Electrical EngineeringKAISTDaejeonRepublic of Korea

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