10.11588/data/XJAOC4Weis, Cleo-AronCleo-AronWeisInstitute of Pathology Mannheim,Medical Faculty Mannheim, Heidelberg University, Heidelberg, GermanyAutomatic evaluation of tumour budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome [Dataset]heiDATA2018Medicine, Health and Life SciencesWeis, Cleo-AronInstitute of Pathology Mannheim,Medical Faculty Mannheim, Heidelberg University, Heidelberg, GermanyWeis, Cleo-AronCleo-AronWeisInstitute of Pathology Mannheim,Medical Faculty Mannheim, Heidelberg University, Heidelberg, GermanyheiDATA: Heidelberg Research Data Repository Heidelberg University2018-06-262018-08-31253078575925034188932641889846218898604188987961889962218896544188922761889515818899308188968941889860218894522767901477043807724450772122877271927720670772830277169327718640771956677246587721062application/octet-streamimage/pngimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiffimage/tiff1.1<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> <br /> 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>. <br /> 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/.