基于生成对抗网络的细胞形变动态分类

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.