CytoGAN: Generative Modeling of Cell Images

By Peter Goldsborough, Nick Pawlowski, Juan C. Caicedo, Shantanu Singh, Anne E Carpenter. 2017

72 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Biophysics, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. James Y. Zou

In Biophysics

1. Gregory R. Johnson
2. Rory M. Donovan-Maiye
3. Mary M. Maleckar
4. James Y. Zou
5. Anvita Gupta

In Media Technology

1. Rory M. Donovan-Maiye
2. James Y. Zou
3. Anvita Gupta
4. Mary M. Maleckar

Citations in the corpus

• Building a 3D Integrated Cell (2017, methodology): “(Goldsborough et al., 2017) uses a GAN to generate images of cells across different conditions and evaluate the performance of a discriminator in identifying the mechanism of action of these conditions.”
• Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions (2018, methodology): “Recently, GANs have been used to morphologically profile cell images [7], to generate time-series ICU data [5], and to generate single cell RNA-seq data from multiple cell types [6].”