Building a Deep Learning-Based Shape Selector

Gaudenz Danuser Lab; Project Mentor: Xinxin Wang

Using computational models to interpret microscopy images can be valuable in studying morphological changes at the cellular level. However, the complexity in the geometry of cells imposes significant difficulties in selecting and calibrating the models to match the experiments either by hand or via simple algorithms. Therefore, this challenge necessitates the need for an autonomous shape selecting pipeline in several ongoing projects in our lab. Here, we invite undergraduate students interested in machine learning to help us design a deep learning-based shape selector that can support our state-of-the-art computational and experimental studies on cell morphology (CM). We expect the selector to: 1) among many computationally generated CM models, determine which one best represents the experimental observation; 2) with help from experts, classify different CMs obtained experimentally into groups. Achieving these two goals will be highly valuable in our endeavour to discover molecular causes of CM changes and their role in cancer progression.

For more information about this project email Xinxin Wang.