Can generative AI replace immunofluorescent staining processes? A comparison study of synthetically generated cellpainting images from brightfield
Author:
Funder
NVIDIA Corp
UKRI
Boehringer Ingelheim Corp USA
Royal Society
H2020
MRC
IMI
NIHR Imperial Biomedical Research Centre
Publisher
Elsevier BV
Reference28 articles.
1. In silico labeling: predicting fluorescent labels in unlabeled images;Christiansen;Cell,2018
2. DeepHCS: bright-field to fluorescence microscopy image conversion using deep learning for label-free high-content screening;Lee,2018
3. DeepHCS++: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening;Lee;Med. Image Anal.,2021
4. Extracting quantitative biological information from bright-field cell images using deep learning;Helgadottir;Biophys. Rev.,2021
5. Label-free prediction of cell painting from brightfield images;Cross-Zamirski;Sci. Rep.,2022
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