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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| Main Author: | |
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| Format: | Database Research Data |
| Language: | English |
| Published: |
Heidelberg
Universität
2018-08-20
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| DOI: | 10.11588/data/XJAOC4 |
| Subjects: | |
| Online Access: | 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 
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| Author Notes: | Cleo-Aron Weis |
MARC
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| 520 | |a 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/. | ||
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