Deep Learning for Imaging Flow Cytometry:

By P. Eulenberg, Niklas Koehler, Thomas Blasi, A. Filby, Anne E Carpenter, Paul Rees, Fabian J Theis, F. Alexander Wolf. 2016

How researchers see this paper

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

1. Travers Ching
2. Alexandr A Kalinin
3. Brett K. Beaulieu-Jones
4. Daniel S. Himmelstein
5. Amr M. Alexandari
6. G. Way
7. Casey S. Greene
8. Brian T Do

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Already, there is evidence deep learning can surpass the efficacy of classical methods [205], even using generic deep convolutional networks trained on natural images [206], known as transfer learning.”
• Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “proposed deep flow model, combining non-linear dimension reduction with CNN, to analyze single cell flow cytometry images [114].”