Training data for the "Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints"
| dc.contributor.affiliation | University of Helsinki - Otso Brummer | |
| dc.contributor.affiliation | Helsinki University Hospital - Oscar Brück | |
| dc.contributor.author | Otso Brummer | |
| dc.contributor.author | Oscar Brück | |
| dc.date.accessioned | 2025-03-24T15:17:15Z | |
| dc.date.issued | 2022-08-15 | |
| dc.date.issued | 2022-08-15 | |
| dc.description | There are two ZIP-files consisting of small histological image tiles that have been used to detect and quantify distinct tissue textures and lymphocyte proportions from H&E-stained clear cell renal cell carcinoma (KIRC) digital tissue sections of the Cancer Genome Atlas (TCGA) image archive and the Helsinki dataset. The tissue_classification file contains 300x300px tissue texture image tiles (n=52,713) representing renal cancer (“cancer”; n=13,057, 24.8%); normal renal (“normal”; n=8,652, 16.4%); stromal (“stroma”; n= 5,460, 10.4%) including smooth muscle, fibrous stroma and blood vessels; red blood cells (“blood”; n=996, 1.9%); empty background (“empty”; n=16,026, 30.4%); and other textures including necrotic, torn and adipose tissue (“other”; n=8,522, 16.2%). Image tiles have been randomly selected from the TCGA-KIRC WSI and the Helsinki datasets. The binary_lymphocytes file contains mostly 256x256px-sized but also smaller image tiles of Low (n=20,092, 80.1%) or High (n=5,003, 19.9%) lymphocyte density (n=25,095). Image tiles have been randomly selected from the TCGA-KIRC WSI dataset. All accuracy of all annotations have been double-checked. However, the classification between multiple tissue textures or lymphocyte density can be sometimes ambiguous. The deep learning model parameters trained with the ResNet-18 infrastructure for (1) lymphocyte and (2) texture classification are named as (1) resnet18_binary_lymphocytes.pth and (2) resnet18_tissue_classification.pth. Codes and instructions to use these are found in https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis. If you use either work, please cite the publication by Brummer O et al (1) AND the TCGA Research Network (2):(1) Brummer, O., Pölönen, P., Mustjoki, S. et al. Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints. Br J Cancer 129, 683–695 (2023). https://doi.org/10.1038/s41416-023-02329-4 (2) The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. | |
| dc.identifier | https://doi.org/10.5281/zenodo.7898308 | |
| dc.identifier.uri | https://hydatakatalogi-test-24.it.helsinki.fi/handle/123456789/10049 | |
| dc.rights | Open | |
| dc.rights.license | cc-by-4.0 | |
| dc.subject | TCGA | |
| dc.subject | clear-cell renal-cell carcinoma | |
| dc.subject | H&E | |
| dc.subject | Tissue textures | |
| dc.subject | Lymphocytes | |
| dc.title | Training data for the "Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints" | |
| dc.type | dataset | |
| dc.type | dataset |