{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:53:50Z","timestamp":1760147630306,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T00:00:00Z","timestamp":1676419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["101017453","104690"],"award-info":[{"award-number":["101017453","104690"]}]},{"name":"Innovate UK on behalf of UK Research and Innovation (UKRI)","award":["101017453","104690"],"award-info":[{"award-number":["101017453","104690"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>In recent years, there has been an increased effort to digitise whole-slide images of cancer tissue. This effort has opened up a range of new avenues for the application of deep learning in oncology. One such avenue is virtual staining, where a deep learning model is tasked with reproducing the appearance of stained tissue sections, conditioned on a different, often times less expensive, input stain. However, data to train such models in a supervised manner where the input and output stains are aligned on the same tissue sections are scarce. In this work, we introduce a dataset of ten whole-slide images of clear cell renal cell carcinoma tissue sections counterstained with Hoechst 33342, CD3, and CD8 using multiple immunofluorescence. We also provide a set of over 600,000 patches of size 256 \u00d7 256 pixels extracted from these images together with cell segmentation masks in a format amenable to training deep learning models. It is our hope that this dataset will be used to further the development of deep learning methods for digital pathology by serving as a dataset for comparing and benchmarking virtual staining models.<\/jats:p>","DOI":"10.3390\/data8020040","type":"journal-article","created":{"date-parts":[[2023,2,16]],"date-time":"2023-02-16T02:28:27Z","timestamp":1676514507000},"page":"40","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0407-7617","authenticated-orcid":false,"given":"Georg","family":"W\u00f6lflein","sequence":"first","affiliation":[{"name":"School of Computer Science, University of St Andrews, North Haugh, St Andrews KY16 9SX, Scotland, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9999-4292","authenticated-orcid":false,"given":"In Hwa","family":"Um","sequence":"additional","affiliation":[{"name":"School of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, Scotland, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9041-9988","authenticated-orcid":false,"given":"David J.","family":"Harrison","sequence":"additional","affiliation":[{"name":"School of Medicine, University of St Andrews, North Haugh, St Andrews KY16 9TF, Scotland, UK"},{"name":"Division of Laboratory Medicine, Lothian NHS University Hospitals, Edinburgh EH16 6SA, Scotland, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9314-194X","authenticated-orcid":false,"given":"Ognjen","family":"Arandjelovi\u0107","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of St Andrews, North Haugh, St Andrews KY16 9SX, Scotland, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"ref_1","unstructured":"Cancer Research UK (2022, September 30). 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