10.11588/data/8LKEZFRunz, MarlenMarlenRunzInstitute of Pathology Mannheim, Medical Faculty Mannheim, Heidelberg University, Heidelberg, GermanyWeis, Cleo-AronCleo-AronWeisInstitute of Pathology Mannheim, Medical Faculty Mannheim, Heidelberg University, Heidelberg, GermanyNormalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks [Dataset]heiDATA2021Computer and Information ScienceMedicine, Health and Life SciencesStain NormalizationRunz, MarlenMarlenRunzInstitute of Pathology Mannheim, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany2021-07-27158672825351834474362112717534063264618398919730174072690017933248798494540787013093972945151296508047740359329603729987661508135154image/pngimage/pngapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zipapplication/zip1.0<p> Here we provide the data sets supporting the experiments in our publication <i>Normalization of HE-Stained Histological Images using Cycle Consistent Generative Adversarial Networks</i>, which were collected at the Institute of Pathology, Medical Faculty Mannheim, Heidelberg University. </p> <p>The HE-Staining Variation (HEV) data set offers serial sections of a follicular thyroid carcinoma, stained with different HE-staining protocols (including name of <b>[stainVariant]</b>): </p> <ol> <li>stained with HE with the standard protocol of the Institute of Pathology, Mannheim (<b>HE</b>)</li> <li>stained too long with HE (<b>longHE</b>)</li> <li>stained too short with HE (<b>shortHE</b>)</li> <li>stained only with Hematoxylin (<b>onlyH</b>)</li> <li>stained only with Eosin (<b>onlyE</b>)</li> <li>stained too long with Hematoxylin (<b>longH</b>)</li> <li>stained too long with Eosin (<b>longE</b>)</li> <li>stained too short with Hematoxylin (<b>shortH</b>)</li> <li>stained too short with Eosin (<b>shortE</b>)</li> </ol> <p> We provided the original whole-slide-images (WSI) in the folder <i>HEV_wsi.zip</i> for each stain-variant. </p> <p> <img src="https://heidata.uni-heidelberg.de/api/access/datafile/4694" alt="wsi_example" width="700"> </p> <p> In addition, for the stain-variants <b>1-5</b> we provide patches (<i>n ~40,000</i> for each set) of size <i>256x256 pixels</i> and split them into 60% train (<i>train_[stainVariant].zip</i>) and 40% test (<i>test_[stainVariant].zip</i>) sets . </p> <p> <img src="https://heidata.uni-heidelberg.de/api/access/datafile/4692" alt="stain_normalization_example" width="600"> </p> <p> Patches from our TumorLymphnode data set for image classification are provided inside <i>tumorLymphnode_patches.zip</i>. It contains <i>~3,600</i> patches of size <i>165x165 pixels</i> for each class normal lymph nodes (<b>normal</b>) and carcinoma infiltration (<b>tumor</b>). </p> <p> The code for our models is available at <a href="http://gitlab.medma.uni-heidelberg.de/digital-pathology/stainTransfer_CycleGAN_pytorch">Gitlab</a>. </p>