Can generative AI replace immunofluorescent staining processes? A comparison study of synthetically generated cellpainting images from brightfield.

导出 LinkOut

翻译打印收藏纠错

作者： Xiaodan,Xing ^[1] ; Siofra,Murdoch ^[1] ; Chunling,Tang ^[2] ; Giorgos,Papanastasiou ^[3] ; Jan,Cross-Zamirski ^[4] ; Yunzhe,Guo ^[2] ; Xianglu,Xiao ^[1] ; Carola-Bibiane,Schönlieb ^[4] ; Yinhai,Wang ^[5] ; Guang,Yang ^[6]

作者单位： Bioengineering Department and Imperial-X, Imperial College London, London, United Kingdom. ^[1] Centre for Craniofacial & Regenerative Biology, King's College London, London, United Kingdom. ^[2] Archimedes Unit, Athena Research Centre, Athens, Greece; School of Computer Science and Electronic Engineering, The University of Essex, Essex, United Kingdom. ^[3] Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom. ^[4] Data Sciences and Quantitative Biology, Discovery Sciences, AstraZeneca R&D, Cambridge, United Kingdom. ^[5] Bioengineering Department and Imperial-X, Imperial College London, London, United Kingdom; National Heart and Lung Institute, Imperial College London, London, United Kingdom; School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom; Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom. Electronic address: gyang@imperial.ac.uk. ^[6]

关键词 Generative models High-throughput screening Immunofluorescence image synthesis

主题词图像处理, 计算机辅助(Image Processing, Computer-Assisted);人类(Humans);算法(Algorithms);荧光抗体技术(Fluorescent Antibody Technique);显微镜检查, 荧光(Microscopy, Fluorescence)

DOI 10.1016/j.compbiomed.2024.109102

PMID 39255659

发布时间 2024-11-13

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交