dc.contributor.author
Aubreville, Marc
dc.contributor.author
Wilm, Frauke
dc.contributor.author
Stathonikos, Nikolas
dc.contributor.author
Breininger, Katharina
dc.contributor.author
Donovan, Taryn A.
dc.contributor.author
Jabari, Samir
dc.contributor.author
Veta, Mitko
dc.contributor.author
Ganz, Jonathan
dc.contributor.author
Ammeling, Jonas
dc.contributor.author
Klopfleisch, Robert
dc.date.accessioned
2023-09-11T06:31:54Z
dc.date.available
2023-09-11T06:31:54Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/40799
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-40520
dc.description.abstract
The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species.
en
dc.format.extent
12 Seiten
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Prognostic markers
en
dc.subject
Translational research
en
dc.subject
Tumour biomarkers
en
dc.subject.ddc
500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie
dc.title
A comprehensive multi-domain dataset for mitotic figure detection
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
484
dcterms.bibliographicCitation.doi
10.1038/s41597-023-02327-4
dcterms.bibliographicCitation.journaltitle
Scientific Data
dcterms.bibliographicCitation.volume
10
dcterms.bibliographicCitation.url
https://doi.org/10.1038/s41597-023-02327-4
refubium.affiliation
Veterinärmedizin
refubium.affiliation.other
Institut für Tierpathologie
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.isPartOf.eissn
2052-4463
refubium.resourceType.provider
WoS-Alert