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Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generation
[article]
2022
arXiv
pre-print
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Automatic generation of medical reports from X-ray images can assist radiologists to perform the time-consuming and yet important reporting task. ...
in X-ray datasets, and the RadLex radiology lexicon. ...
We can first filter out sentences of negative or inconclusive mentions using publicly available tools, i.e., clinical entity relationship parser RadGraph 2 [31] and radiology entity extraction RadLex-Annotator ...
arXiv:2207.01208v2
fatcat:qqheb2oe6zffjgilqwb2hjqoia
ViLMedic: a framework for research at the intersection of vision and language in medical AI
2022
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
unpublished
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
As of 2022, the library contains a dozen reference implementations replicating the state-of-the-art results for problems that range from medical visual question answering and radiology report generation ...
Ultimately, we hope our reproducible pipelines can enable clinical translation and create real impact. The library is available at https://github. com/jbdel/vilmedic. ...
report, a Named Entity Recognition accuracy based on the medical NER model of Stanza (Qi et al., 2020) and the RadGraph (Jain, Saahil et al., 2021) reward that scores the similarities between the ...
doi:10.18653/v1/2022.acl-demo.3
fatcat:lmmxnicjxfg5vgucoby63bjoja