Your browser doesn't support javascript.
loading
Capturing Single-Cell Phenotypic Variation via Unsupervised Representation Learning.
Lafarge, Maxime W; Caicedo, Juan C; Carpenter, Anne E; Pluim, Josien P W; Singh, Shantanu; Veta, Mitko.
Afiliação
  • Lafarge MW; Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
  • Caicedo JC; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Carpenter AE; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Pluim JPW; Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
  • Singh S; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Veta M; Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Proc Mach Learn Res ; 103: 315-325, 2019 Jul.
Article em En | MEDLINE | ID: mdl-35874600
We propose a novel variational autoencoder (VAE) framework for learning representations of cell images for the domain of image-based profiling, important for new therapeutic discovery. Previously, generative adversarial network-based (GAN) approaches were proposed to enable biologists to visualize structural variations in cells that drive differences in populations. However, while the images were realistic, they did not provide direct reconstructions from representations, and their performance in downstream analysis was poor. We address these limitations in our approach by adding an adversarial-driven similarity constraint applied to the standard VAE framework, and a progressive training procedure that allows higher quality reconstructions than standard VAE's. The proposed models improve classification accuracy by 22% (to 90%) compared to the best reported GAN model, making it competitive with other models that have higher quality representations, but lack the ability to synthesize images. This provides researchers a new tool to match cellular phenotypes effectively, and also to gain better insight into cellular structure variations that are driving differences between populations of cells.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article