MétaCan
Menu
Cohort builder

4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

Search term
Author
Year range
Sort
Language
Type
Field
Venue
Topic
AI in cancer detection
Retraction
Abstract
Evidence source
Study design
Label agreement
Label status

Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
fundfunder
venuejournal
aboutaboutness

The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,463 results · 1 filter active ·
Results by year
20002025
Publication date
Categories
Machine labels · sparse coverage
Evidence
Language
Type
Citations
An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,463 works in the cohort · of 4,299,418page 3 of 30

Labels cover 4 of 1,463 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 1,463 of 1,463 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

afffundunlabeled
Computational pathology: A survey review and the way forward
Mahdi S. Hosseini, Babak Ehteshami Bejnordi, Vincent Quoc‐Huy Trinh, Lyndon Chan, Danial Hasan, Xingwen Li +15 more
2024· review· en· Journal of Pathology Informatics· Computer Science
distilled prediction:candidate · noneconsensus · none
96
citations
affno abstractunlabeled
Analysis of 3D pathology samples using weakly supervised AI
Andrew H. Song, Mane Williams, Drew F. K. Williamson, Sarah S. L. Chow, Guillaume Jaume, Gan Gao +13 more
2024· article· en· Cell· Computer Science
distilled prediction:candidate · noneconsensus · none
94
citations
affunlabeled
Colorectal Cancer Detection Based on Deep Learning
Lin Xu, Blair Walker, Peir‐In Liang, Yi Xin Tong, Cheng Xu, Yu Su +1 more
2020· article· en· Journal of Pathology Informatics· Computer Science
distilled prediction:candidate · noneconsensus · none
83
citations
fundno affunlabeled
Digital Microscopy, Image Analysis, and Virtual Slide Repository
Famke Aeffner, Hibret A. Adissu, Michael C. Boyle, Robert D. Cardiff, Erik Hagendorn, Mark J. Hoenerhoff +5 more
2018· article· en· ILAR Journal· Computer Science
distilled prediction:candidate · scholarly_communicationconsensus · none
75
citations
affunlabeled
The history of pathology informatics: A global perspective
Seung Park, Anil V. Parwani, Raymond D. Aller, L Banach, Michael J. Becich, Stephan Borkenfeld +12 more
2013· article· en· Journal of Pathology Informatics· Computer Science
distilled prediction:candidate · noneconsensus · none
70
citations

How this was built: Screen · Findings · About