MétaCan
ENFR
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.

Export CSVExport JSONAPI query

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 24 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. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affno abstractunlabeled
Improved Inference via Deep Input Transfer
Saeid Asgari Taghanaki, Kumar Abhishek, Ghassan Hamarneh
2019· preprint· en· Lecture notes in computer science· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Appendix B: Examples of Phantom and Test Tools for Mammography QC
Martin J. Yaffe, P. C. Bunch, L. Desponds, Roberta A. Jong, Robert M. Nishikawa, M. J. Tapiovaara +1 more
2009· article· en· Journal of the ICRU· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
affno abstractunlabeled
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V (biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology)
Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, María A. Zuluaga, Shuchang Zhou +2 more
2020· preprint· en· HAL (Le Centre pour la Communication Scientifique Directe)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Clinical Dataset SE objects - GSE20194
Benjamin Haibe‐Kains
2022· dataset· en· Zenodo (CERN European Organization for Nuclear Research)· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Role in Medicine for Digital Pathology
Bernard Têtu, Lewis Hassell
2015· book-chapter· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Learning autoencoded radon projections
Aditya Sriram, Shivam Kalra, Hamid R. Tizhoosh, Shahryar Rahnamayan
2017· preprint· en· Computer Science
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1016/b978-1-4160-5909-7.00040-0
2000· book-chapter· en· Time to knit· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Facilities for Mammography Image Evaluation
Hiroshi Wada, Satoshi Hirata, Sunao Ikeue, Hiroko Sugano, Hiromitsu Akabane, Akihiko Numata +3 more
2013· article· en· Nihon Nyugan Kenshin Gakkaishi (Journal of Japan Association of Breast Cancer Screening)· Computer Science
machine prediction:candidate · insufficient_payloadconsensus · none
0
citations
← previousnext →

How this was built: Screen · Findings · About