An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate
  Characterization of Tumor Immune Microenvironment
Parmida Ghahremani Joseph Marino Juan Hernandez-Prera Janis V. de la Iglesia Robbert JC Slebos Christine H. Chung Saad Nadeem
We introduce a new AI-ready computational pathology dataset containing
restained and co-registered digitized images from eight head-and-neck squamous
cell carcinoma patients. Specifically, the same tumor sections were stained
with the expensive multiplex immunofluorescence (mIF) assay first and then
restained with cheaper multiplex immunohistochemistry (mIHC). This is a first
public dataset that demonstrates the equivalence of these two staining methods
which in turn allows several use cases; due to the equivalence, our cheaper
mIHC staining protocol can offset the need for expensive mIF staining/scanning
which requires highly-skilled lab technicians. As opposed to subjective and
error-prone immune cell annotations from individual pathologists (disagreement
> 50%) to drive SOTA deep learning approaches, this dataset provides objective
immune and tumor cell annotations via mIF/mIHC restaining for more reproducible
and accurate characterization of tumor immune microenvironment (e.g. for
immunotherapy). We demonstrate the effectiveness of this dataset in three use
cases: (1) IHC quantification of CD3/CD8 tumor-infiltrating lymphocytes via
style transfer, (2) virtual translation of cheap mIHC stains to more expensive
mIF stains, and (3) virtual tumor/immune cellular phenotyping on standard
hematoxylin images. The dataset is available at
\url{https://github.com/nadeemlab/DeepLIIF}.