Automatic evaluation of tumour budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome [Dataset] (ICPSR doi:10.11588/data/XJAOC4)

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Document Description

Citation

Title:

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

Identification Number:

doi:10.11588/data/XJAOC4

Distributor:

heiDATA

Date of Distribution:

2018-08-20

Version:

1

Bibliographic Citation:

Weis, Cleo-Aron, 2018, "Automatic evaluation of tumour budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome [Dataset]", https://doi.org/10.11588/data/XJAOC4, heiDATA, V1

Study Description

Citation

Title:

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

Identification Number:

doi:10.11588/data/XJAOC4

Authoring Entity:

Weis, Cleo-Aron (Institute of Pathology Mannheim,Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany)

Producer:

Weis, Cleo-Aron

Distributor:

heiDATA

Date of Distribution:

2018-08-20

Study Scope

Keywords:

Medicine, Health and Life Sciences

Abstract:

<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/.

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2018-06-26_database_CNNTrainingAndValidation.mat

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