基于生成对抗网络的细胞形变动态分类

Translated title of the contribution: Classification of Cell Deformation Dynamics Based on Generative Adversarial Networks

Feng Qian Pang, Zhi Wen Liu*, Yong Gang Shi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

A generative adversarial networks (GANs) based model was proposed to classify cell deformation dynamics. In the framework, an auxiliary classifier GANs (AC-GANs) were introduced to simultaneously train GANs and a classification network for cell deformation dynamics in live-cell videos. The generated samples from GANs could further enhance the performance of the original classification network. To facilitate application of GANs, cell dynamic image was used to encapsulate the cell dynamics in videos along the temporal dimension, making the cell dynamics information mapped from video area to image area for the construction of the GANs. Then, the classification information was employed in AC-GANs to improve the generation of multi-class samples for GANs, and these multi-class samples could enhance the performance of classification net for improving the cell dynamic deformation. Experimental results demonstrate that the proposed pipeline can effectively capture the spatio-temporal cell dynamics from the raw live-cell videos and outperforms existing methods on the live-cell database.

Translated title of the contributionClassification of Cell Deformation Dynamics Based on Generative Adversarial Networks
Original languageChinese (Traditional)
Pages (from-to)33-37
Number of pages5
JournalBeijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume39
DOIs
Publication statusPublished - 1 Jun 2019

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