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Found 7 papers, 2 papers with code
A Decade in a Systematic Review: The Evolution and Impact of Cell Painting
High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions.
Cell Painting Gallery: an open resource for image-based profiling
Image-based or morphological profiling is a rapidly expanding field wherein cells are "profiled" by extracting hundreds to thousands of unbiased, quantitative features from images of cells that have been perturbed by genetic or chemical perturbations.
Reproducible image-based profiling with Pycytominer
Technological advances in high-throughput microscopy have facilitated the acquisition of cell images at a rapid pace, and data pipelines can now extract and process thousands of image-based features from microscopy images.
Pseudo-Labeling Enhanced by Privileged Information and Its Application to In Situ Sequencing Images
Various strategies for label-scarce object detection have been explored by the computer vision research community.
CellProfiler Analyst 3.0: Accessible data exploration and machine learning for image analysis
Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens.
Weakly Supervised Learning of Single-Cell Feature Embeddings
Many new applications in drug discovery and functional genomics require capturing the morphology of individual imaged cells as comprehensively as possible rather than measuring one particular feature.
Applying Faster R-CNN for Object Detection on Malaria Images
Deep learning based models have had great success in object detection, but the state of the art models have not yet been widely applied to biological image data.
