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

Jae-Hyeok Lee, Seong Tae Kim, Hakmin Lee, Yong Man Ro; Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018, pp. 0-0

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 wellbehaved 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.