Deep learning approach to hologram based cellular classification

Abstract: With the rise of data intensive classification algorithms the need for high throughput imagery methods has increased. Lens-free imagery provides a new high throughput technique for imaging cells through hologram measurements. One acquisition of a Petri dish can provide between one thousand and ten thousand samples which do not need to be annotated if the biological properties of the Petri dish are known. Previously, hologram classification was addressed using feature extraction and non-linear classifier. In this work a deep learning approach to cellular classification using holograms is introduced. Because deep learning approaches do not require hand tailored features they are quicker to develop and the framework is easier to generalize to other hologram classification tasks. A dataset containing alive and dead cell holograms was used to judge the feasibility of the approach. Although the deep learning classifier was successful in classifying simulated holograms (over 97% test accuracy), the experimental dataset showed some key flaws which limited test performance. In an effort to improve deep learning approach the necessary improvements to create a better experimental dataset suited for deep learninghave also been identified.