AAII Seminar: March 4, 2020

Title: Methods for Few-Shot Biomedical Image Segmentation and Learning Equations from Data

Abstract: Machine learning methods have had a powerful impact in the field of biomedical image segmentation and analysis. Traditionally, these machine learning methods have had two drawbacks: they do not ensure contiguity of a segmented object, and they require many expensive manually annotated images to train. I will present a novel methodology for image segmentation by reformulating the machine learning method to trace the boundary of an object, the way a human would. We explore the accuracy of the method on benchmark cell segmentation datasets as well as in-house ‘real’ data. We further compare the ability of the method to work in the low-data limit and show that our method can be used to generate training data for more sophisticated segmentation methods, thereby reducing the burden of human annotation. I will then explore a hybrid framework the combines elements from dynamical systems and machine learning to analyze the quantifiable data generated from such image segmentations. In particular, I will present a robust method for learning underlying dynamical systems from noisy spatiotemporal data, drawing from examples in cell migration and cancer.