Efficient end-to-end learning for cell segmentation with machine generated weak annotations.

ABSTRACT: Automated cell segmentation from optical microscopy images is usually the first step in the pipeline of single-cell analysis. Recently, deep-learning based algorithms have shown superior performances for the cell segmentation tasks. However, a disadvantage of deep-learning is the requirement for a large amount of fully annotated training data, which is costly to generate. Weakly-supervised and self-supervised learning is an active research area, but often the model accuracy is inversely correlated with the amount of annotation information provided. Here we focus on a specific subtype of weak annotations, which can be generated programmably from experimental data, thus allowing for more annotation information content without sacrificing the annotation speed. We designed a new model architec

SUBMITTER: Shrestha P 

PROVIDER: S-EPMC9981753 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature