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Whole slide images and patches of clear cell renal cell carcinoma tissue sections counterstained with Hoechst 33342, CD3, and CD8 using multiple immunofluorescence
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dc.contributor.author | Wolflein, Georg | |
dc.contributor.author | Um, In Hwa | |
dc.contributor.author | Harrison, David James | |
dc.contributor.author | Arandelovic, Oggie | |
dc.date.accessioned | 2023-02-15T13:30:08Z | |
dc.date.available | 2023-02-15T13:30:08Z | |
dc.date.issued | 2023-02-15 | |
dc.identifier | 283321094 | |
dc.identifier | 55eaf123-e4bd-4b09-b700-0ce896a7dba3 | |
dc.identifier | 85148945119 | |
dc.identifier.citation | Wolflein , 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 . https://doi.org/10.3390/data8020040 | en |
dc.identifier.issn | 2306-5729 | |
dc.identifier.other | ORCID: /0000-0001-9041-9988/work/129146060 | |
dc.identifier.other | ORCID: /0000-0002-0407-7617/work/129148160 | |
dc.identifier.other | ORCID: /0000-0001-9999-4292/work/158122923 | |
dc.identifier.other | ORCID: /0000-0002-9314-194X/work/164895915 | |
dc.identifier.uri | https://hdl.handle.net/10023/26986 | |
dc.description | Funding: 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.abstract | 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 × 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.format.extent | 9 | |
dc.format.extent | 2424690 | |
dc.language.iso | eng | |
dc.relation.ispartof | Data | en |
dc.subject | Cancer | en |
dc.subject | Digital pathology | en |
dc.subject | Machine learning | en |
dc.subject | Computer vision | en |
dc.subject | Virtual staining | en |
dc.subject | Segmentation | en |
dc.subject | RC0254 Neoplasms. Tumors. Oncology (including Cancer) | en |
dc.subject | RB Pathology | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | 3rd-DAS | en |
dc.subject | SDG 3 - Good Health and Well-being | en |
dc.subject | MCC | en |
dc.subject.lcc | RC0254 | en |
dc.subject.lcc | RB | en |
dc.subject.lcc | QA75 | en |
dc.title | Whole slide images and patches of clear cell renal cell carcinoma tissue sections counterstained with Hoechst 33342, CD3, and CD8 using multiple immunofluorescence | en |
dc.type | Journal article | en |
dc.contributor.sponsor | European Commission | en |
dc.contributor.sponsor | Innovate UK | en |
dc.contributor.institution | University of St Andrews. School of Computer Science | en |
dc.contributor.institution | University of St Andrews. School of Medicine | en |
dc.contributor.institution | University of St Andrews. Cellular Medicine Division | en |
dc.contributor.institution | University of St Andrews. Sir James Mackenzie Institute for Early Diagnosis | en |
dc.identifier.doi | 10.3390/data8020040 | |
dc.description.status | Peer reviewed | en |
dc.identifier.grantnumber | 101017453 | en |
dc.identifier.grantnumber | TS/S013121/1 | en |
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