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Part 1: Document Description
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Citation |
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Title: |
Automatic evaluation of tumour budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome [Dataset] |
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Identification Number: |
doi:10.11588/data/XJAOC4 |
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Distributor: |
heiDATA |
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Date of Distribution: |
2018-08-20 |
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Version: |
1 |
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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 |
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Citation |
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Title: |
Automatic evaluation of tumour budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome [Dataset] |
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Identification Number: |
doi:10.11588/data/XJAOC4 |
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Authoring Entity: |
Weis, Cleo-Aron (Institute of Pathology Mannheim,Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany) |
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Producer: |
Weis, Cleo-Aron |
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Distributor: |
heiDATA |
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Distributor: |
heiDATA: Heidelberg Research Data Repository |
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Access Authority: |
Weis, Cleo-Aron |
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Date of Deposit: |
2018-06-26 |
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Holdings Information: |
https://doi.org/10.11588/data/XJAOC4 |
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Study Scope |
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Keywords: |
Medicine, Health and Life Sciences |
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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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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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2018-06-26_database_CNNTrainingAndValidation.mat |
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application/octet-stream |
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AbbildungHeiData.png |
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image/png |
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CRC_TA_I_Core1.tiff |
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image/tiff |
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CRC_TA_I_Core16.tiff |
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CRC_TA_I_Core19.tiff |
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CRC_TA_I_Core2.tiff |
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CRC_TA_I_Core34.tiff |
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CRC_TA_I_Core4.tiff |
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CRC_TA_I_Core42.tiff |
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CRC_TA_I_Core58.tiff |
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CRC_TA_I_Core7.tiff |
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CRC_TA_VI_Core1797.tiff |
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CRC_TA_V_Core1775.tiff |
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CRC_TA_V_Core1785.tiff |
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GraMa030_core00010.tiff |
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GraMa030_core00068.tiff |
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GraMa030_core00118.tiff |
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GraMa030_core00136.tiff |
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GraMa119_core00005.tiff |
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GraMa119_core00068.tiff |
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GraMa119_core00098.tiff |
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GraMa233_core00139.tiff |
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GraMa233_core00150.tiff |
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