Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for AI-generated Radiology Reports-Reference-Cited by-同舟云学术

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Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for AI-generated Radiology Reports

Published:2024-06-03 Issue: Volume:1 Page:402-411
ISSN:
Container-title:2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)
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Author:

Zhu Qingqing¹,Chen Xiuying²,Jin Qiao¹,Hou Benjamin³,Mathai Tejas Sudharshan³,Mukherjee Pritam³,Gao Xin²,Summers Ronald M.³,Lu Zhiyong¹

Affiliation:

1. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health,Bethesda,MD,USA

2. Bioscience Reseach Center, King Abdullah University of Science & Technology,Saudi Arabia

3. National Institutes of Health Clinical Center,Imaging Biomarkers and Computer-Aided Diagnosis Laboratory,Department of Radiology and Imaging Sciences,Bethesda,MD,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10628445/10628534/10628824.pdf?arnumber=10628824

Reference42 articles.

1. Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation

2. A Topic-aware Summarization Framework with Different Modal Side Information

3. Im-proving the robustness of summarization systems with dual augmentation;Chen;ACL,2023

4. Multimodal contrastive learning for radiology report generation;Wu;Journal of Ambient Intelligence and Humanized Computing,2022

5. CheXPrune: sparse chest X-ray report generation model using multi-attention and one-shot global pruning

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