论文标题
DeepLiif：量化临床病理幻灯片的在线平台
DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides
论文作者
论文摘要
在诊所中，切除的组织样品用苏木精和欧世毒素（H＆E）和/或免疫组织化学（IHC）染色（IHC）染色，并在载玻片上或作为数字扫描作为诊断和评估疾病进展的数字扫描。细胞级定量，例如在IHC蛋白表达评分中，可能极低效率和主观。我们提出了DeepLiif（https://deepliif.org），这是第一个免费的在线平台，用于有效且可重现的IHC评分。 DeepLiif的表现优于当前最新方法（依赖手动错误的注释），它实际上是休息的临床IHC幻灯片，并具有更有信息的多重免疫荧光染色。 Our DeepLIIF cloud-native platform supports (1) more than 150 proprietary/non-proprietary input formats via the Bio-Formats standard, (2) interactive adjustment, visualization, and downloading of the IHC quantification results and the accompanying restained images, (3) consumption of an exposed workflow API programmatically or through interactive plugins for open source whole slide image viewers such as QuPath/ImageJ, and (4) auto根据用户需求，扩展到有效扩展GPU资源。
In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin (H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists on glass slides or as digital scans for diagnosis and assessment of disease progression. Cell-level quantification, e.g. in IHC protein expression scoring, can be extremely inefficient and subjective. We present DeepLIIF (https://deepliif.org), a first free online platform for efficient and reproducible IHC scoring. DeepLIIF outperforms current state-of-the-art approaches (relying on manual error-prone annotations) by virtually restaining clinical IHC slides with more informative multiplex immunofluorescence staining. Our DeepLIIF cloud-native platform supports (1) more than 150 proprietary/non-proprietary input formats via the Bio-Formats standard, (2) interactive adjustment, visualization, and downloading of the IHC quantification results and the accompanying restained images, (3) consumption of an exposed workflow API programmatically or through interactive plugins for open source whole slide image viewers such as QuPath/ImageJ, and (4) auto scaling to efficiently scale GPU resources based on user demand.
