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Optimizing Deep Neural Networks for Single Cell Segmentation


Cui, Can (Sunny) (2020) Optimizing Deep Neural Networks for Single Cell Segmentation. Senior thesis (Major), California Institute of Technology. doi:10.7907/030b-pm67.


Analysis of live-cell imaging experiments at the resolution of single cells provides exciting insights into the inner workings of biological systems. Advances in biological imaging and computer vision allow for segmentation of natural images with a high degree of accuracy. However, automation of the segmentation pipeline at the single cell resolution remains a challenging task. Complex deep learning models require large, well-annotated datasets that are rarely available in biology. In this research, we explore various methods that optimize state of the art deep learning frameworks, despite limited resources. We trained a large permutation of models to quantify their capacity and to measure the effects of temporal information, spatial awareness and transfer learning on model performance. We find that, although training set size is most impactful in improving model accuracy, we can leverage techniques like spatial awareness and transfer learning to compromise for the lack of data. These insights show that, with an abundance of data, light-weight models can be as performant as their heavy-weight counterparts in cellular analysis.

Item Type:Thesis (Senior thesis (Major))
Subject Keywords:Deep learning, computational biology, single cell segmentation
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Electrical Engineering
Awards:Mabel Beckman Prize, 2020. Donald S. Clark Memorial Award, 2019.
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Van Valen, David A.
Thesis Committee:
  • None, None
Defense Date:9 June 2020
Record Number:CaltechTHESIS:06052020-210957529
Persistent URL:
Cui, Can (Sunny)0000-0003-4665-0626
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:13782
Deposited By: Can Cui
Deposited On:12 Jun 2020 21:36
Last Modified:12 Jun 2020 21:36

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