Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset]

DOI

Here we provide the data sets supporting the experiments in our publication Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks, which were collected at the Institute of Pathology, Medical Faculty Mannheim, Heidelberg University.

The HE-Staining Variation (HEV) data set offers serial sections of a follicular thyroid carcinoma, stained with different HE-staining protocols (including name of [stainVariant]):

stained with HE with the standard protocol of the Institute of Pathology, Mannheim (HE) stained too long with HE (longHE) stained too short with HE (shortHE) stained only with Hematoxylin (onlyH) stained only with Eosin (onlyE) stained too long with Hematoxylin (longH) stained too long with Eosin (longE) stained too short with Hematoxylin (shortH) stained too short with Eosin (shortE)

We provided the original whole-slide-images (WSI) in the folder HEV_wsi.zip for each stain-variant.

In addition, for the stain-variants 1-5 we provide patches (n ~40,000 for each set) of size 256x256 pixels and split them into 60% train (train_[stainVariant].zip) and 40% test (test_[stainVariant].zip) sets .

Patches from our TumorLymphnode data set for image classification are provided inside tumorLymphnode_patches.zip. It contains ~3,600 patches of size 165x165 pixels for each class normal lymph nodes (normal) and carcinoma infiltration (tumor).

The code for our models is available at Gitlab.

Identifier
DOI https://doi.org/10.11588/data/8LKEZF
Metadata Access https://heidata.uni-heidelberg.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.11588/data/8LKEZF
Provenance
Creator Runz, Marlen; Weis, Cleo-Aron
Publisher heiDATA
Contributor Runz, Marlen
Publication Year 2021
Rights info:eu-repo/semantics/openAccess
OpenAccess true
Contact Runz, Marlen (Institute of Pathology Mannheim, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany)
Representation
Resource Type Dataset
Format image/png; application/zip
Size 1586728; 253518; 3447436211; 2717534063; 2646183989; 1973017407; 2690017933; 2487984945; 4078701309; 3972945151; 2965080477; 4035932960; 3729987661; 508135154
Version 1.0
Discipline Life Sciences; Medicine