Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation
Abstract
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.
Cite
Text
Lee et al. "Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation." European Conference on Computer Vision Workshops, 2018. doi:10.1007/978-3-030-11024-6_22Markdown
[Lee et al. "Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation." European Conference on Computer Vision Workshops, 2018.](https://mlanthology.org/eccvw/2018/lee2018eccvw-feature2mass/) doi:10.1007/978-3-030-11024-6_22BibTeX
@inproceedings{lee2018eccvw-feature2mass,
title = {{Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation}},
author = {Lee, Jae-Hyeok and Kim, Seong Tae and Lee, Hakmin and Ro, Yong Man},
booktitle = {European Conference on Computer Vision Workshops},
year = {2018},
pages = {326-334},
doi = {10.1007/978-3-030-11024-6_22},
url = {https://mlanthology.org/eccvw/2018/lee2018eccvw-feature2mass/}
}