RadLex: a new method for indexing online educational materials.
By Curtis P. Langlotz. Radiographics, 2006
465 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Language and Linguistics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/3ad228df2adc69c97c4e02174f4956893445290e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ozanan R. Meireles
- Weidong (Tom) Cai
- Lena Maier-Hein
- Nigam H. Shah
- Daniel Richard Leff
- Nassir Navab
- D. Feng
- Gregory Hager
In Language and Linguistics
In Molecular Biology
- Weidong (Tom) Cai
- Nigam H. Shah
- D. Feng
- Gregory Hager
- Natasha Noy
- Michael J. Fulham
- Ashnil Kumar
- Jinman Kim
Citations in the corpus
- Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data (2013, methodology): “Furthermore, the integration of medical terminology in ontologies such as RadLex [80] and the Unified Medical Language System [111] by learning correspondences between image features and text labels should also be investigated for the case of multidimensional images.”
- Surgical data science – from concepts toward clinical translation (2020, background): “Several relevant ontologies with potential use in surgery such as the Foundational Model of Anatomy (FMA), SNOMED CT or RadLex (Langlotz, 2006) are already available.”
- Biomedical ontologies: a functional perspective (2007, background): “RadLex is a controlled terminology for radiology, providing terms for the techniques, findings, and diseases associated with medical images [28].”