Liis Salumäe

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile

0Author DataRank

Indexed papers

0in pilot corpus

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 2 papers. Top paper: “HAPPY: A deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images”.

HAPPY: A deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.11.21.517353

N/A

0DataRank · unranked

5 citations

Jelisaveta Džigurski, Valentina Rukins, Omri Dodi, Siim Siigur, Liis Salumäe +10 more

Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY

Nature Communications(2024)10.1038/s41467-024-46986-2

N/A

0DataRank · unranked

33 citations

Jelisaveta Džigurski, Valentina Rukins, Omri Dodi, Siim Siigur, Liis Salumäe +10 more