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

TL;DR

This paper introduces a novel method for generating realistic labeled bio-mass images by processing visual features in latent space, addressing issues of realism and diversity in bio-image synthesis.

Contribution

It presents a new approach for bio-image generation that improves realism and variation without requiring additional label annotation tasks.

Findings

Generated images appeared realistic and diverse.

The method effectively captured targeted mass characteristics.

Experimental results demonstrated improved image quality.

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.