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The analysis of FFPE tissue sections stained with haematoxylin and eosin(H&E) or immunohistochemistry (IHC) is an essential part of the pathologicassessment of surgically resected breast cancer specimens. IHC staining hasbeen broadly adopted into diagnostic guidelines and routine workflows tomanually assess status and scoring of several established biomarkers, includingER, PGR, HER2 and KI67. However, this is a task that can also be facilitated bycomputational pathology image analysis methods. The research in computationalpathology has recently made numerous substantial advances, often based onpublicly available whole slide image (WSI) data sets. However, the field isstill considerably limited by the sparsity of public data sets. In particular,there are no large, high quality publicly available data sets with WSIs ofmatching IHC and H&E-stained tissue sections. Here, we publish the currentlylargest publicly available data set of WSIs of tissue sections from surgicalresection specimens from female primary breast cancer patients with matchedWSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from1,153 patients. The primary purpose of the data set was to facilitate theACROBAT WSI registration challenge, aiming at accurately aligning H&E and IHCimages. For research in the area of image registration, automatic quantitativefeedback on registration algorithm performance remains available through theACROBAT challenge website, based on more than 37,000 manually annotatedlandmark pairs from 13 annotators. Beyond registration, this data set has thepotential to enable many different avenues of computational pathology research,including stain-guided learning, virtual staining, unsupervised pre-training,artefact detection and stain-independent models.

ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology