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Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generation [article]

Sixing Yan, William K. Cheung, Keith Chiu, Terence M. Tong, Charles K. Cheung, Simon See
2022 arXiv   pre-print
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

Jean-benoit Delbrouck, Khaled Saab, Maya Varma, Sabri Eyuboglu, Pierre Chambon, Jared Dunnmon, Juan Zambrano, Akshay Chaudhari, Curtis Langlotz
2022 Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations   unpublished
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