Cited by 199Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

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Radiology report generation with a learned knowledge base and multi-modal alignment

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Yang, Shuxin, Wu, Xian, Ge, Shen, Zheng, Zhuozhao, Zhou, S. Kevin, Xiao, Li·2023·Medical Image Analysis

DOI 10.1016/j.media.2023.102798

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Abstract

In clinics, a radiology report is crucial for guiding a patient's treatment. However, writing radiology reports is a heavy burden for radiologists. To this end, we present an automatic, multi-modal approach for report generation from a chest x-ray. Our approach, motivated by the observation that the descriptions in radiology reports are highly correlated with specific information of the x-ray images, features two distinct modules: (i) Learned knowledge base: To absorb the knowledge embedded in the radiology reports, we build a knowledge base that can automatically distill and restore medical knowledge from textual embedding without manual labor; (ii) Multi-modal alignment: to promote the semantic alignment among reports, disease labels, and images, we explicitly utilize textual embedding to guide the learning of the visual feature space. We evaluate the performance of the proposed model using metrics from both natural language generation and clinic efficacy on the public IU-Xray and MIMIC-CXR datasets. Our ablation study shows that each module contributes to improving the quality of generated reports. Furthermore, the assistance of both modules, our approach outperforms state-of-the-art methods over almost all the metrics. Code is available at https://github.com/LX-doctorAI1/M2KT.

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Authors: Yang, S., Wu, X., Ge, S., Zheng, Z., Zhou, S. K., & Xiao, L.
Affiliations: Chinese Academy of Sciences (CN)
Tencent (China) (CN)
Beijing Tsinghua Chang Gung Hospital (CN)
University of Science and Technology of China (CN)
Beijing University of Posts and Telecommunications (CN)
Corresponding author: Xiao, Li
ORCID: Yang, Shuxin
Wu, Xian
Ge, Shen
Zheng, Zhuozhao
Zhou, S. Kevin
Xiao, Li
Venue: Medical Image Analysis
Publisher: Elsevier BV
Published: 2023-05
Volume: 86
Pages: 102798
ISSN: 1361-8415 (ISSN-L)
DOI: 10.1016/j.media.2023.102798
OpenAlex: W4360604432
Cited by: 199
References: 47
Citations by year: 2026: 58 · 2025: 89 · 2024: 41 · 2023: 11
FWCI: 18.03
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Medical Image Analysis (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University) (cc-by)
Funders: Tencent
National Natural Science Foundation of China (31900979)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: radiology report generation, chest radiography, knowledge distillation, multimodal alignment, text embeddings, semantic alignment, natural language generation
MeSH terms: Humans, Learning, Radiography, Radiology, Benchmarking, Knowledge Bases

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Radiology report generation with a learned knowledge base and multi-modal alignment

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