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

<b> Data used for the implementation of the proposed tumor budding detection</b><br />
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: . <br />
Step 1: Color and size based segmentation. <br />
Step 2: Validation of the detected objects (proposals) by a spatial clustering and a convolutional neural network (MatConvNet by A. Vedaldi et al. [1]). <br />

<p><img src="https://heidata.uni-heidelberg.de/api/access/datafile/1772?imageThumb=400&pfdrid_c=true"></p>

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The Matlab-Code for the project is available on <a href="https://github.com/catweis/Automatic-evaluation-of-tumour-budding-in-immunohistochemically-stained-colorectal-carcinomas-">GitHub</a>.
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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”).<br />
Please refer to the "Terms" tab below for usage and reproduction terms.<br />
<b> References:</b><br />
1. Vedaldi, A., K. Lenc, and A. Gupta. MatConvNet: CNNs for MATLAB. 2015; Available from: http://www.vlfeat.org/matconvnet/.