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Automatic evaluation of tumour budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome [dataset]

Data used for the implementation of the proposed tumor budding detection In 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 immunohistochemica...

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Bibliographische Detailangaben
1. Verfasser: Weis, Cleo-Aron Thias (VerfasserIn)
Dokumenttyp: Datenbank Forschungsdaten
Sprache:Englisch
Veröffentlicht: Heidelberg Universität 2018-08-20
DOI:10.11588/data/XJAOC4
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Forschungsdaten
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Online-Zugang:Verlag, kostenfrei, Volltext: http://dx.doi.org/10.11588/data/XJAOC4
Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/XJAOC4
Volltext
Verfasserangaben:Cleo-Aron Weis
Beschreibung
Zusammenfassung:Data used for the implementation of the proposed tumor budding detection In 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: . Step 1: Color and size based segmentation. Step 2: Validation of the detected objects (proposals) by a spatial clustering and a convolutional neural network (MatConvNet by A. Vedaldi et al. [1]). The Matlab-Code for the project is available on GitHub. The data for the CNN-training and validation are presented as .mat-file. It contains a struct element with the images in a 4D-matrix, the label (“bud” and “no bud”) and a set (“training” and “validation”). Please refer to the "Terms" tab below for usage and reproduction terms. References: 1. Vedaldi, A., K. Lenc, and A. Gupta. MatConvNet: CNNs for MATLAB. 2015; Available from: http://www.vlfeat.org/matconvnet/.
Beschreibung:Deposit date: 2018-06-26
Gesehen am 24.01.2019
Beschreibung:Online Resource
DOI:10.11588/data/XJAOC4

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