DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides
This provides a tool for pathologists to improve efficiency and reproducibility in clinical pathology, though it appears incremental as it builds on existing staining and quantification methods.
The authors tackled the problem of inefficient and subjective cell-level quantification in immunohistochemistry (IHC) scoring by developing DeepLIIF, an online platform that outperforms state-of-the-art approaches by virtually restaining clinical IHC slides with multiplex immunofluorescence staining.
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.