Springer Nature

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MIDOG++.sqlite (4.43 MB)

MIDOG++ database in SlideRunner sqlite format

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posted on 2023-06-30, 13:39 authored by Marc Aubreville, Frauke Wilm, Nikolas Stathonikos, Katharina Breininger, Taryn Donovan, Samir Jabari, Mitko Veta, Jonathan Ganz, Jonas Ammeling, P. J. van Diest, Robert Klopfleisch, Christof Bertram
The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.