A Modular and Interpretable Pipeline for Unsupervised Learning on Scientific Spatiotemporal Imaging Datasets

Timothée Levilly, Zhen Yuan Yeo, Lim Ying Chen, Ngoc Thi Nguyen, Alexandre H. Thiery, N. Duane Loh

Submission Type: I want my submission to be considered for poster only

Keywords: bioimaging, software, unsupervised learning

TL;DR: We describe an unsupervised learning pipeline architecture for spatiotemporal data with modularity and interpretability in mind for many potential applications.

Confirmation Of Submission Requirements: I submit an abstract. It uses the template provided on the submission page and is no longer than 2 pages.

Submission Number: 15