{"dcterms:modified":"2024-01-18","dcterms:creator":"heiDATA","@type":"ore:ResourceMap","schema:additionalType":"Dataverse OREMap Format v1.0.0","dvcore:generatedBy":{"@type":"schema:SoftwareApplication","schema:name":"Dataverse","schema:version":"6.1 build 1590-f5d1299","schema:url":"https://github.com/iqss/dataverse"},"@id":"https://heidata.uni-heidelberg.de/api/datasets/export?exporter=OAI_ORE&persistentId=https://doi.org/10.11588/data/XJAOC4","ore:describes":{"author":{"citation:authorName":"Weis, Cleo-Aron","citation:authorAffiliation":"Institute of Pathology Mannheim,Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany","authorIdentifierScheme":"ORCID"},"citation:producer":{"citation:producerName":"Weis, Cleo-Aron","citation:producerAffiliation":"Institute of Pathology Mannheim,Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany"},"citation:distributor":{"citation:distributorName":"heiDATA: Heidelberg Research Data Repository ","citation:distributorAffiliation":"Heidelberg University","citation:distributorURL":"https://heidata.uni-heidelberg.de"},"citation:dsDescription":{"citation:dsDescriptionValue":" Data used for the implementation of the proposed tumor budding detection
\r\nIn the publication “Automatic evaluation of tumour budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome” we described a multistep approach to detect tumor buds in immunohistochemically stained images: .
\r\nStep 1: Color and size based segmentation.
\r\nStep 2: Validation of the detected objects (proposals) by a spatial clustering and a convolutional neural network (MatConvNet by A. Vedaldi et al. [1]).
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