DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides
Abstract
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.
Cite
Text
Ghahremani et al. "DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides." Conference on Computer Vision and Pattern Recognition, 2022. doi:10.1109/CVPR52688.2022.02071Markdown
[Ghahremani et al. "DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides." Conference on Computer Vision and Pattern Recognition, 2022.](https://mlanthology.org/cvpr/2022/ghahremani2022cvpr-deepliif/) doi:10.1109/CVPR52688.2022.02071BibTeX
@inproceedings{ghahremani2022cvpr-deepliif,
title = {{DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides}},
author = {Ghahremani, Parmida and Marino, Joseph and Dodds, Ricardo and Nadeem, Saad},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2022},
pages = {21399-21405},
doi = {10.1109/CVPR52688.2022.02071},
url = {https://mlanthology.org/cvpr/2022/ghahremani2022cvpr-deepliif/}
}