Development of a multi-scanner facility for data acquisition for digital pathology artificial intelligence Article Swipe
Matthew P. Humphries
,
Danny Kaye
,
Gaby Stankeviciute
,
Jacob Halliwell
,
Alexander Wright
,
Daljeet Bansal
,
D S Brettle
,
Darren Treanor
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.11.07.23297408
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.11.07.23297408
Whole slide imaging (WSI) of pathology glass slides with high-resolution scanners has enabled the large-scale application of artificial intelligence (AI) in pathology, to support the detection and diagnosis of disease, potentially increasing efficiency and accuracy in tissue diagnosis. Despite the promise of AI, it has limitations. “Brittleness” or sensitivity to variation in inputs necessitates that large amounts of data are used for training. AI is often trained on data from different scanners but not usually by replicating the same slide across scanners. The utilisation of multiple WSI instruments to produce digital replicas of the same glass slides will make more comprehensive datasets and may improve the robustness and generalisability of AI algorithms as well as reduce the overall data requirements of AI training. To this end, the National Pathology Imagine Cooperative (NPIC) has built the AI FORGE ( F acilitating O pportunities for R obust G eneralisable data E mulation), a unique multi-scanner facility embedded in a clinical site in the NHS to (a) compare scanner performance and (b) replicate digital pathology image datasets across WSI systems. The NPIC AI FORGE currently comprises 15 scanners from 9 manufacturers. It can generate approximately 4000 WSI images per day (approximately 7Tb of image data). This paper describes the process followed to plan and build such a facility.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.11.07.23297408
- https://www.medrxiv.org/content/medrxiv/early/2023/11/07/2023.11.07.23297408.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388450344
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4388450344Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2023.11.07.23297408Digital Object Identifier
- Title
-
Development of a multi-scanner facility for data acquisition for digital pathology artificial intelligenceWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-07Full publication date if available
- Authors
-
Matthew P. Humphries, Danny Kaye, Gaby Stankeviciute, Jacob Halliwell, Alexander Wright, Daljeet Bansal, D S Brettle, Darren TreanorList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.11.07.23297408Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2023/11/07/2023.11.07.23297408.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2023/11/07/2023.11.07.23297408.full.pdfDirect OA link when available
- Concepts
-
Scanner, Digital pathology, Computer science, Data acquisition, Artificial intelligence, Data science, Computer vision, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
13Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4388450344 |
|---|---|
| doi | https://doi.org/10.1101/2023.11.07.23297408 |
| ids.doi | https://doi.org/10.1101/2023.11.07.23297408 |
| ids.openalex | https://openalex.org/W4388450344 |
| fwci | 0.51088578 |
| type | preprint |
| title | Development of a multi-scanner facility for data acquisition for digital pathology artificial intelligence |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10862 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.991100013256073 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | AI in cancer detection |
| topics[1].id | https://openalex.org/T12422 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9423999786376953 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2741 |
| topics[1].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[1].display_name | Radiomics and Machine Learning in Medical Imaging |
| topics[2].id | https://openalex.org/T11636 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.906499981880188 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2718 |
| topics[2].subfield.display_name | Health Informatics |
| topics[2].display_name | Artificial Intelligence in Healthcare and Education |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2779751349 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7667257785797119 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1474480 |
| concepts[0].display_name | Scanner |
| concepts[1].id | https://openalex.org/C2777522853 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5497114062309265 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q5276128 |
| concepts[1].display_name | Digital pathology |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5258758068084717 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C163985040 |
| concepts[3].level | 2 |
| concepts[3].score | 0.473358154296875 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1172399 |
| concepts[3].display_name | Data acquisition |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.3877030313014984 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C2522767166 |
| concepts[5].level | 1 |
| concepts[5].score | 0.3540129065513611 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[5].display_name | Data science |
| concepts[6].id | https://openalex.org/C31972630 |
| concepts[6].level | 1 |
| concepts[6].score | 0.32059091329574585 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[6].display_name | Computer vision |
| concepts[7].id | https://openalex.org/C111919701 |
| concepts[7].level | 1 |
| concepts[7].score | 0.0651601254940033 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[7].display_name | Operating system |
| keywords[0].id | https://openalex.org/keywords/scanner |
| keywords[0].score | 0.7667257785797119 |
| keywords[0].display_name | Scanner |
| keywords[1].id | https://openalex.org/keywords/digital-pathology |
| keywords[1].score | 0.5497114062309265 |
| keywords[1].display_name | Digital pathology |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5258758068084717 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/data-acquisition |
| keywords[3].score | 0.473358154296875 |
| keywords[3].display_name | Data acquisition |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.3877030313014984 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/data-science |
| keywords[5].score | 0.3540129065513611 |
| keywords[5].display_name | Data science |
| keywords[6].id | https://openalex.org/keywords/computer-vision |
| keywords[6].score | 0.32059091329574585 |
| keywords[6].display_name | Computer vision |
| keywords[7].id | https://openalex.org/keywords/operating-system |
| keywords[7].score | 0.0651601254940033 |
| keywords[7].display_name | Operating system |
| language | en |
| locations[0].id | doi:10.1101/2023.11.07.23297408 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402567 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| locations[0].source.host_organization | https://openalex.org/I2750212522 |
| locations[0].source.host_organization_name | Cold Spring Harbor Laboratory |
| locations[0].source.host_organization_lineage | https://openalex.org/I2750212522 |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://www.medrxiv.org/content/medrxiv/early/2023/11/07/2023.11.07.23297408.full.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.1101/2023.11.07.23297408 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5077990086 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1306-7012 |
| authorships[0].author.display_name | Matthew P. Humphries |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I2799390153 |
| authorships[0].affiliations[0].raw_affiliation_string | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I130828816 |
| authorships[0].affiliations[1].raw_affiliation_string | University of Leeds, Leeds, UK |
| authorships[0].institutions[0].id | https://openalex.org/I2799390153 |
| authorships[0].institutions[0].ror | https://ror.org/00v4dac24 |
| authorships[0].institutions[0].type | healthcare |
| authorships[0].institutions[0].lineage | https://openalex.org/I2799390153 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | Leeds Teaching Hospitals NHS Trust |
| authorships[0].institutions[1].id | https://openalex.org/I130828816 |
| authorships[0].institutions[1].ror | https://ror.org/024mrxd33 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I130828816 |
| authorships[0].institutions[1].country_code | GB |
| authorships[0].institutions[1].display_name | University of Leeds |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Matthew P. Humphries |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK, University of Leeds, Leeds, UK |
| authorships[1].author.id | https://openalex.org/A5070921703 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Danny Kaye |
| authorships[1].countries | GB |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I2799390153 |
| authorships[1].affiliations[0].raw_affiliation_string | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I130828816 |
| authorships[1].affiliations[1].raw_affiliation_string | University of Leeds, Leeds, UK |
| authorships[1].institutions[0].id | https://openalex.org/I2799390153 |
| authorships[1].institutions[0].ror | https://ror.org/00v4dac24 |
| authorships[1].institutions[0].type | healthcare |
| authorships[1].institutions[0].lineage | https://openalex.org/I2799390153 |
| authorships[1].institutions[0].country_code | GB |
| authorships[1].institutions[0].display_name | Leeds Teaching Hospitals NHS Trust |
| authorships[1].institutions[1].id | https://openalex.org/I130828816 |
| authorships[1].institutions[1].ror | https://ror.org/024mrxd33 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I130828816 |
| authorships[1].institutions[1].country_code | GB |
| authorships[1].institutions[1].display_name | University of Leeds |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Danny Kaye |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK, University of Leeds, Leeds, UK |
| authorships[2].author.id | https://openalex.org/A5092946381 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Gaby Stankeviciute |
| authorships[2].countries | GB |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I130828816 |
| authorships[2].affiliations[0].raw_affiliation_string | University of Leeds, Leeds, UK |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I2799390153 |
| authorships[2].affiliations[1].raw_affiliation_string | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK |
| authorships[2].institutions[0].id | https://openalex.org/I2799390153 |
| authorships[2].institutions[0].ror | https://ror.org/00v4dac24 |
| authorships[2].institutions[0].type | healthcare |
| authorships[2].institutions[0].lineage | https://openalex.org/I2799390153 |
| authorships[2].institutions[0].country_code | GB |
| authorships[2].institutions[0].display_name | Leeds Teaching Hospitals NHS Trust |
| authorships[2].institutions[1].id | https://openalex.org/I130828816 |
| authorships[2].institutions[1].ror | https://ror.org/024mrxd33 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I130828816 |
| authorships[2].institutions[1].country_code | GB |
| authorships[2].institutions[1].display_name | University of Leeds |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Gaby Stankeviciute |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK, University of Leeds, Leeds, UK |
| authorships[3].author.id | https://openalex.org/A5089259532 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Jacob Halliwell |
| authorships[3].countries | GB |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I130828816 |
| authorships[3].affiliations[0].raw_affiliation_string | University of Leeds, Leeds, UK |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I2799390153 |
| authorships[3].affiliations[1].raw_affiliation_string | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK |
| authorships[3].institutions[0].id | https://openalex.org/I2799390153 |
| authorships[3].institutions[0].ror | https://ror.org/00v4dac24 |
| authorships[3].institutions[0].type | healthcare |
| authorships[3].institutions[0].lineage | https://openalex.org/I2799390153 |
| authorships[3].institutions[0].country_code | GB |
| authorships[3].institutions[0].display_name | Leeds Teaching Hospitals NHS Trust |
| authorships[3].institutions[1].id | https://openalex.org/I130828816 |
| authorships[3].institutions[1].ror | https://ror.org/024mrxd33 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I130828816 |
| authorships[3].institutions[1].country_code | GB |
| authorships[3].institutions[1].display_name | University of Leeds |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Jacob Halliwell |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK, University of Leeds, Leeds, UK |
| authorships[4].author.id | https://openalex.org/A5020968144 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-3310-1145 |
| authorships[4].author.display_name | Alexander Wright |
| authorships[4].countries | GB |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I2799390153 |
| authorships[4].affiliations[0].raw_affiliation_string | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK |
| authorships[4].affiliations[1].institution_ids | https://openalex.org/I130828816 |
| authorships[4].affiliations[1].raw_affiliation_string | University of Leeds, Leeds, UK |
| authorships[4].institutions[0].id | https://openalex.org/I2799390153 |
| authorships[4].institutions[0].ror | https://ror.org/00v4dac24 |
| authorships[4].institutions[0].type | healthcare |
| authorships[4].institutions[0].lineage | https://openalex.org/I2799390153 |
| authorships[4].institutions[0].country_code | GB |
| authorships[4].institutions[0].display_name | Leeds Teaching Hospitals NHS Trust |
| authorships[4].institutions[1].id | https://openalex.org/I130828816 |
| authorships[4].institutions[1].ror | https://ror.org/024mrxd33 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I130828816 |
| authorships[4].institutions[1].country_code | GB |
| authorships[4].institutions[1].display_name | University of Leeds |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Alexander I Wright |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK, University of Leeds, Leeds, UK |
| authorships[5].author.id | https://openalex.org/A5006224951 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Daljeet Bansal |
| authorships[5].countries | GB |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I130828816 |
| authorships[5].affiliations[0].raw_affiliation_string | University of Leeds, Leeds, UK |
| authorships[5].affiliations[1].institution_ids | https://openalex.org/I2799390153 |
| authorships[5].affiliations[1].raw_affiliation_string | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK |
| authorships[5].institutions[0].id | https://openalex.org/I2799390153 |
| authorships[5].institutions[0].ror | https://ror.org/00v4dac24 |
| authorships[5].institutions[0].type | healthcare |
| authorships[5].institutions[0].lineage | https://openalex.org/I2799390153 |
| authorships[5].institutions[0].country_code | GB |
| authorships[5].institutions[0].display_name | Leeds Teaching Hospitals NHS Trust |
| authorships[5].institutions[1].id | https://openalex.org/I130828816 |
| authorships[5].institutions[1].ror | https://ror.org/024mrxd33 |
| authorships[5].institutions[1].type | education |
| authorships[5].institutions[1].lineage | https://openalex.org/I130828816 |
| authorships[5].institutions[1].country_code | GB |
| authorships[5].institutions[1].display_name | University of Leeds |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Daljeet Bansal |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK, University of Leeds, Leeds, UK |
| authorships[6].author.id | https://openalex.org/A5080158125 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-6963-1757 |
| authorships[6].author.display_name | D S Brettle |
| authorships[6].countries | GB |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I130828816 |
| authorships[6].affiliations[0].raw_affiliation_string | University of Leeds, Leeds, UK |
| authorships[6].affiliations[1].institution_ids | https://openalex.org/I2799390153 |
| authorships[6].affiliations[1].raw_affiliation_string | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK |
| authorships[6].institutions[0].id | https://openalex.org/I2799390153 |
| authorships[6].institutions[0].ror | https://ror.org/00v4dac24 |
| authorships[6].institutions[0].type | healthcare |
| authorships[6].institutions[0].lineage | https://openalex.org/I2799390153 |
| authorships[6].institutions[0].country_code | GB |
| authorships[6].institutions[0].display_name | Leeds Teaching Hospitals NHS Trust |
| authorships[6].institutions[1].id | https://openalex.org/I130828816 |
| authorships[6].institutions[1].ror | https://ror.org/024mrxd33 |
| authorships[6].institutions[1].type | education |
| authorships[6].institutions[1].lineage | https://openalex.org/I130828816 |
| authorships[6].institutions[1].country_code | GB |
| authorships[6].institutions[1].display_name | University of Leeds |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | David Brettle |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK, University of Leeds, Leeds, UK |
| authorships[7].author.id | https://openalex.org/A5001235418 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-4579-484X |
| authorships[7].author.display_name | Darren Treanor |
| authorships[7].countries | GB, SE |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I130828816 |
| authorships[7].affiliations[0].raw_affiliation_string | University of Leeds, Leeds, UK |
| authorships[7].affiliations[1].institution_ids | https://openalex.org/I2799390153 |
| authorships[7].affiliations[1].raw_affiliation_string | National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK |
| authorships[7].affiliations[2].institution_ids | https://openalex.org/I102134673 |
| authorships[7].affiliations[2].raw_affiliation_string | Department of Clinical Pathology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden |
| authorships[7].affiliations[3].institution_ids | https://openalex.org/I102134673 |
| authorships[7].affiliations[3].raw_affiliation_string | Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden |
| authorships[7].affiliations[4].institution_ids | https://openalex.org/I2799390153 |
| authorships[7].affiliations[4].raw_affiliation_string | Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK |
| authorships[7].institutions[0].id | https://openalex.org/I2799390153 |
| authorships[7].institutions[0].ror | https://ror.org/00v4dac24 |
| authorships[7].institutions[0].type | healthcare |
| authorships[7].institutions[0].lineage | https://openalex.org/I2799390153 |
| authorships[7].institutions[0].country_code | GB |
| authorships[7].institutions[0].display_name | Leeds Teaching Hospitals NHS Trust |
| authorships[7].institutions[1].id | https://openalex.org/I130828816 |
| authorships[7].institutions[1].ror | https://ror.org/024mrxd33 |
| authorships[7].institutions[1].type | education |
| authorships[7].institutions[1].lineage | https://openalex.org/I130828816 |
| authorships[7].institutions[1].country_code | GB |
| authorships[7].institutions[1].display_name | University of Leeds |
| authorships[7].institutions[2].id | https://openalex.org/I102134673 |
| authorships[7].institutions[2].ror | https://ror.org/05ynxx418 |
| authorships[7].institutions[2].type | education |
| authorships[7].institutions[2].lineage | https://openalex.org/I102134673 |
| authorships[7].institutions[2].country_code | SE |
| authorships[7].institutions[2].display_name | Linköping University |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Darren Treanor |
| authorships[7].is_corresponding | True |
| authorships[7].raw_affiliation_strings | Centre for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden, Department of Clinical Pathology and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden, Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK, National Pathology Imaging Cooperative, Leeds Teaching Hospitals NHS Trust, Leeds, UK, University of Leeds, Leeds, UK |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.medrxiv.org/content/medrxiv/early/2023/11/07/2023.11.07.23297408.full.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Development of a multi-scanner facility for data acquisition for digital pathology artificial intelligence |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10862 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.991100013256073 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | AI in cancer detection |
| related_works | https://openalex.org/W4232857084, https://openalex.org/W4245233074, https://openalex.org/W2167384162, https://openalex.org/W2001485867, https://openalex.org/W4402492658, https://openalex.org/W1517555227, https://openalex.org/W2653993779, https://openalex.org/W2911295078, https://openalex.org/W762151464, https://openalex.org/W4321437783 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1101/2023.11.07.23297408 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402567 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| best_oa_location.source.host_organization | https://openalex.org/I2750212522 |
| best_oa_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://www.medrxiv.org/content/medrxiv/early/2023/11/07/2023.11.07.23297408.full.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.1101/2023.11.07.23297408 |
| primary_location.id | doi:10.1101/2023.11.07.23297408 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402567 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| primary_location.source.host_organization | https://openalex.org/I2750212522 |
| primary_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| primary_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://www.medrxiv.org/content/medrxiv/early/2023/11/07/2023.11.07.23297408.full.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/2023.11.07.23297408 |
| publication_date | 2023-11-07 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W4206999655, https://openalex.org/W2398520201, https://openalex.org/W2509553455, https://openalex.org/W1976435325, https://openalex.org/W3113891464, https://openalex.org/W3198525581, https://openalex.org/W2763065411, https://openalex.org/W3186679119, https://openalex.org/W2952971376, https://openalex.org/W4385330064, https://openalex.org/W3126702715, https://openalex.org/W2901782819, https://openalex.org/W3128713593 |
| referenced_works_count | 13 |
| abstract_inverted_index.( | 138 |
| abstract_inverted_index.9 | 187 |
| abstract_inverted_index.E | 149 |
| abstract_inverted_index.F | 139 |
| abstract_inverted_index.G | 146 |
| abstract_inverted_index.O | 141 |
| abstract_inverted_index.R | 144 |
| abstract_inverted_index.a | 151, 157, 214 |
| abstract_inverted_index.15 | 184 |
| abstract_inverted_index.AI | 64, 111, 122, 136, 180 |
| abstract_inverted_index.It | 189 |
| abstract_inverted_index.To | 124 |
| abstract_inverted_index.as | 113, 115 |
| abstract_inverted_index.by | 76 |
| abstract_inverted_index.in | 21, 36, 52, 156, 160 |
| abstract_inverted_index.is | 65 |
| abstract_inverted_index.it | 44 |
| abstract_inverted_index.of | 5, 17, 29, 42, 58, 85, 93, 110, 121, 200 |
| abstract_inverted_index.on | 68 |
| abstract_inverted_index.or | 48 |
| abstract_inverted_index.to | 23, 50, 89, 163, 209 |
| abstract_inverted_index.(a) | 164 |
| abstract_inverted_index.(b) | 169 |
| abstract_inverted_index.7Tb | 199 |
| abstract_inverted_index.AI, | 43 |
| abstract_inverted_index.NHS | 162 |
| abstract_inverted_index.The | 83, 178 |
| abstract_inverted_index.WSI | 87, 176, 194 |
| abstract_inverted_index.and | 27, 34, 103, 108, 168, 211 |
| abstract_inverted_index.are | 60 |
| abstract_inverted_index.but | 73 |
| abstract_inverted_index.can | 190 |
| abstract_inverted_index.day | 197 |
| abstract_inverted_index.for | 62, 143 |
| abstract_inverted_index.has | 12, 45, 133 |
| abstract_inverted_index.may | 104 |
| abstract_inverted_index.not | 74 |
| abstract_inverted_index.per | 196 |
| abstract_inverted_index.the | 14, 25, 40, 78, 94, 106, 117, 127, 135, 161, 206 |
| abstract_inverted_index.(AI) | 20 |
| abstract_inverted_index.4000 | 193 |
| abstract_inverted_index.NPIC | 179 |
| abstract_inverted_index.This | 203 |
| abstract_inverted_index.data | 59, 69, 119, 148 |
| abstract_inverted_index.end, | 126 |
| abstract_inverted_index.from | 70, 186 |
| abstract_inverted_index.make | 99 |
| abstract_inverted_index.more | 100 |
| abstract_inverted_index.plan | 210 |
| abstract_inverted_index.same | 79, 95 |
| abstract_inverted_index.site | 159 |
| abstract_inverted_index.such | 213 |
| abstract_inverted_index.that | 55 |
| abstract_inverted_index.this | 125 |
| abstract_inverted_index.used | 61 |
| abstract_inverted_index.well | 114 |
| abstract_inverted_index.will | 98 |
| abstract_inverted_index.with | 9 |
| abstract_inverted_index.(WSI) | 4 |
| abstract_inverted_index.FORGE | 137, 181 |
| abstract_inverted_index.Whole | 1 |
| abstract_inverted_index.build | 212 |
| abstract_inverted_index.built | 134 |
| abstract_inverted_index.glass | 7, 96 |
| abstract_inverted_index.image | 173, 201 |
| abstract_inverted_index.large | 56 |
| abstract_inverted_index.obust | 145 |
| abstract_inverted_index.often | 66 |
| abstract_inverted_index.paper | 204 |
| abstract_inverted_index.slide | 2, 80 |
| abstract_inverted_index.(NPIC) | 132 |
| abstract_inverted_index.across | 81, 175 |
| abstract_inverted_index.data). | 202 |
| abstract_inverted_index.images | 195 |
| abstract_inverted_index.inputs | 53 |
| abstract_inverted_index.reduce | 116 |
| abstract_inverted_index.slides | 8, 97 |
| abstract_inverted_index.tissue | 37 |
| abstract_inverted_index.unique | 152 |
| abstract_inverted_index.Despite | 39 |
| abstract_inverted_index.Imagine | 130 |
| abstract_inverted_index.amounts | 57 |
| abstract_inverted_index.compare | 165 |
| abstract_inverted_index.digital | 91, 171 |
| abstract_inverted_index.enabled | 13 |
| abstract_inverted_index.imaging | 3 |
| abstract_inverted_index.improve | 105 |
| abstract_inverted_index.overall | 118 |
| abstract_inverted_index.process | 207 |
| abstract_inverted_index.produce | 90 |
| abstract_inverted_index.promise | 41 |
| abstract_inverted_index.scanner | 166 |
| abstract_inverted_index.support | 24 |
| abstract_inverted_index.trained | 67 |
| abstract_inverted_index.usually | 75 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.National | 128 |
| abstract_inverted_index.accuracy | 35 |
| abstract_inverted_index.clinical | 158 |
| abstract_inverted_index.datasets | 102, 174 |
| abstract_inverted_index.disease, | 30 |
| abstract_inverted_index.embedded | 155 |
| abstract_inverted_index.facility | 154 |
| abstract_inverted_index.followed | 208 |
| abstract_inverted_index.generate | 191 |
| abstract_inverted_index.multiple | 86 |
| abstract_inverted_index.replicas | 92 |
| abstract_inverted_index.scanners | 11, 72, 185 |
| abstract_inverted_index.systems. | 177 |
| abstract_inverted_index.Pathology | 129 |
| abstract_inverted_index.comprises | 183 |
| abstract_inverted_index.currently | 182 |
| abstract_inverted_index.describes | 205 |
| abstract_inverted_index.detection | 26 |
| abstract_inverted_index.diagnosis | 28 |
| abstract_inverted_index.different | 71 |
| abstract_inverted_index.facility. | 215 |
| abstract_inverted_index.pathology | 6, 172 |
| abstract_inverted_index.replicate | 170 |
| abstract_inverted_index.scanners. | 82 |
| abstract_inverted_index.training. | 63, 123 |
| abstract_inverted_index.variation | 51 |
| abstract_inverted_index.algorithms | 112 |
| abstract_inverted_index.artificial | 18 |
| abstract_inverted_index.diagnosis. | 38 |
| abstract_inverted_index.efficiency | 33 |
| abstract_inverted_index.increasing | 32 |
| abstract_inverted_index.mulation), | 150 |
| abstract_inverted_index.pathology, | 22 |
| abstract_inverted_index.robustness | 107 |
| abstract_inverted_index.Cooperative | 131 |
| abstract_inverted_index.acilitating | 140 |
| abstract_inverted_index.application | 16 |
| abstract_inverted_index.instruments | 88 |
| abstract_inverted_index.large-scale | 15 |
| abstract_inverted_index.performance | 167 |
| abstract_inverted_index.potentially | 31 |
| abstract_inverted_index.replicating | 77 |
| abstract_inverted_index.sensitivity | 49 |
| abstract_inverted_index.utilisation | 84 |
| abstract_inverted_index.eneralisable | 147 |
| abstract_inverted_index.intelligence | 19 |
| abstract_inverted_index.limitations. | 46 |
| abstract_inverted_index.necessitates | 54 |
| abstract_inverted_index.pportunities | 142 |
| abstract_inverted_index.requirements | 120 |
| abstract_inverted_index.approximately | 192 |
| abstract_inverted_index.comprehensive | 101 |
| abstract_inverted_index.multi-scanner | 153 |
| abstract_inverted_index.(approximately | 198 |
| abstract_inverted_index.manufacturers. | 188 |
| abstract_inverted_index.high-resolution | 10 |
| abstract_inverted_index.generalisability | 109 |
| abstract_inverted_index.“Brittleness” | 47 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5001235418 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I102134673, https://openalex.org/I130828816, https://openalex.org/I2799390153 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.69135379 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |