Drorith Hochner‐Celnikier

MedicineComputer Science

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 3 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

Biologically Inspired Digital Histology for Deep Phenotyping of Placental Composition Changes Across Major Lesion Types

bioRxiv (Cold Spring Harbor Laboratory)(2025)10.64898/2025.12.22.693945

N/A

0DataRank · unranked

0 citations

Claudia Vanea, Karen Meir, Drorith Hochner‐Celnikier, Hagit Hochner, Triin Laisk +5 more