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 …

Learning robust representations to discriminate cell phenotypes based on microscopy
images is important for drug discovery. Drug development efforts typically analyse …

Motivation The identification and discovery of phenotypes from high content screening
images is a challenging task. Earlier works use image analysis pipelines to extract biological …

Motivation Advances in automation and imaging have made it possible to capture a large
image dataset that spans multiple experimental batches of data. However, accurate …

Neuroimaging data are an increasingly important part of etiological studies of neurological
and psychiatric disorders. However, mitigating the influence of nuisance variables, including …

Neuroimaging data are a vital part of advancing our understanding of brain health and the
etiology of neuropsychiatric disorders. But the inherent complexity and dimensionality of …

Computer vision (CV) approaches applied to digital pathology have informed biological
discovery and development of tools to help inform clinical decision-making. However, batch …

In neuroimaging studies, multivariate methods provide a framework for studying
associations between complex patterns distributed throughout the brain and neurological …