Name
Abstract | ||
|---|---|---|
Health and life sciences' research fields make an intensive use of graphical information to represent complex relations between biological entities, called molecular pathways. Interpretation of molecular pathway diagrams requires domain-specific knowledge to remove ambiguity. We propose a fully automatic method that detects entities and extracts their relations from diagrams. It uses image analysis and a domain-specific cognitive model and automatically produces a structured textual version of the content. Results on an annotated dataset show precision of 0.99 and recall of up to 0.85 for the detection of molecular entities and interactions. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICDAR.2017.253 | 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) |
Keywords | Field | DocType |
molecular pathway diagrams,domain-specific knowledge,image analysis,domain-specific cognitive model,structured textual version,molecular entities,graphical information,complex relations,biological entities,molecular pathway interaction extraction,health science,life science | Task analysis,Pattern recognition,Computer science,Image segmentation,Molecular pathway,Artificial intelligence,Cognitive model,Recall,Ambiguity,Optical character recognition software | Conference |
Volume | ISSN | ISBN |
02 | 1520-5363 | 978-1-5386-3587-2 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Foncubierta-Rodriguez | 1 | 156 | 17.13 |
| Anca-Nicoleta Ciubotaru | 2 | 0 | 0.68 |
| Costas Bekas | 3 | 81 | 12.75 |
| Maria Gabrani | 4 | 2 | 7.92 |