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dc.contributor.authorWolflein, Georg
dc.contributor.authorUm, In Hwa
dc.contributor.authorHarrison, David James
dc.contributor.authorArandelovic, Oggie
dc.identifier.citationWolflein , G , Um , I H , Harrison , D J & Arandelovic , O 2023 , ' Whole slide images and patches of clear cell renal cell carcinoma tissue sections counterstained with Hoechst 33342, CD3, and CD8 using multiple immunofluorescence ' , Data , vol. 8 , no. 2 , 40 .
dc.identifier.otherPURE: 283321094
dc.identifier.otherPURE UUID: 55eaf123-e4bd-4b09-b700-0ce896a7dba3
dc.identifier.otherORCID: /0000-0001-9041-9988/work/129146060
dc.identifier.otherORCID: /0000-0002-0407-7617/work/129148160
dc.identifier.otherScopus: 85148945119
dc.descriptionFunding: G.W. is supported by Lothian NHS. This project received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 101017453 as part of the KATY project. This work is supported in part by the Industrial Centre for AI Research in Digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) (project number 104690).en
dc.description.abstractIn 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 × 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.
dc.rightsCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 4.0/).en
dc.subjectDigital pathologyen
dc.subjectMachine learningen
dc.subjectComputer visionen
dc.subjectVirtual stainingen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectRB Pathologyen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.titleWhole slide images and patches of clear cell renal cell carcinoma tissue sections counterstained with Hoechst 33342, CD3, and CD8 using multiple immunofluorescenceen
dc.typeJournal articleen
dc.contributor.sponsorEuropean Commissionen
dc.contributor.sponsorInnovate UKen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Cellular Medicine Divisionen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.description.statusPeer revieweden

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