Image-based profiling is a maturing strategy by which the rich information present in
biological images is reduced to a multidimensional profile, a collection of extracted image …

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic
differences among a variety of cell populations. It paves the way to studying biological …

Many biological applications require the segmentation of cell bodies, membranes and nuclei
from microscopy images. Deep learning has enabled great progress on this problem, but …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

Automatic detection and segmentation of cells and nuclei in microscopy images is important
for many biological applications. Recent successful learning-based approaches include per …

Fast and selective isolation of single cells with unique spatial and morphological traits
remains a technical challenge. Here, we address this by establishing high-speed image …

Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative
analysis of imaging data for biological and biomedical applications. Many bioimage analysis …

Comprehensively charting the biologically causal circuits that govern the phenotypic space
of human cells has often been viewed as an insurmountable challenge. However, in the last …

Increasing evidence demonstrated that the expression of Angiotensin I-Converting Enzyme
type 2 (ACE2) is a necessary step for SARS-CoV-2 infection permissiveness. In light of the …

Modern drug discovery approaches often use high-content imaging to systematically study
the effect on cells of large libraries of chemical compounds. By automatically screening …