Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles.

ABSTRACT: Cell and organelle shape are driven by diverse genetic and environmental factors and thus accurate quantification of cellular morphology is essential to experimental cell biology. Autoencoders are a popular tool for unsupervised biological image analysis because they learn a low-dimensional representation that maps images to feature vectors to generate a semantically meaningful embedding space of morphological variation. The learned feature vectors can also be used for clustering, dimensionality reduction, outlier detection, and supervised learning problems. Shape properties do not change with orientation, and thus we argue that representation learning methods should encode this orientation invariance. We show that conventional autoencoders are sensitive to orientation, which can lead to s

SUBMITTER: Burgess J 

PROVIDER: S-EPMC10838319 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature