生物信息学
免疫组织化学
病理
淀粉样蛋白(真菌学)
生物
计算生物学
计算机科学
医学
遗传学
基因
作者
Bryan He,Syed A. Bukhari,Edward Fox,Abubakar Abid,Jeanne Shen,Claudia H. Kawas,María M. Corrada,Thomas J. Montine,James Zou
标识
DOI:10.1016/j.crmeth.2022.100191
摘要
We develop a deep learning approach, in silico immunohistochemistry (IHC), which takes routinely collected histochemical-stained samples as input and computationally generates virtual IHC slide images. We apply in silico IHC to Alzheimer's disease samples, where several hallmark changes are conventionally identified using IHC staining across many regions of the brain. In silico IHC computationally identifies neurofibrillary tangles, β-amyloid plaques, and neuritic plaques at a high spatial resolution directly from the histochemical images, with areas under the receiver operating characteristic curve of between 0.88 and 0.92. In silico IHC learns to identify subtle cellular morphologies associated with these lesions and can generate in silico IHC slides that capture key features of the actual IHC.
科研通智能强力驱动
Strongly Powered by AbleSci AI