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Interactive and xxplainable region-guided radiology report generation.
In: (IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 17-24 June 2023, Vancouver, BC, Canada). 10662 Los Vaqueros Circle, Po Box 3014, Los Alamitos, Ca 90720-1264 Usa: Ieee Computer Soc, 2023. 7433-7442 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition ; 2023-June)
The automatic generation of radiology reports has the potential to assist radiologists in the time-consuming task of report writing. Existing methods generate the full report from image-level features, failing to explicitly focus on anatomical regions in the image. We propose a simple yet effective region-guided report generation model that detects anatomical regions and then describes individual, salient regions to form the final report. While previous methods generate reports without the possibility of human intervention and with limited explainability, our method opens up novel clinical use cases through additional interactive capabilities and introduces a high degree of transparency and explainability. Comprehensive experiments demonstrate our method's effectiveness in report generation, outperforming previous state-of-the-art models, and highlight its interactive capabilities. The code and checkpoints are available at https://github.com/ttanida/rgrg.
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Publication type
Article: Conference contribution
Keywords
Cell Microscopy ; Medical And Biological Vision
Language
english
Publication Year
2023
HGF-reported in Year
2023
ISSN (print) / ISBN
1063-6919
Conference Title
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Conference Date
17-24 June 2023
Conference Location
Vancouver, BC, Canada
Quellenangaben
Volume: 2023-June,
Pages: 7433-7442
Publisher
Ieee Computer Soc
Publishing Place
10662 Los Vaqueros Circle, Po Box 3014, Los Alamitos, Ca 90720-1264 Usa
Institute(s)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute for Machine Learning in Biomed Imaging (IML)
Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-530014-001
G-507100-001
G-507100-001
WOS ID
001058542607076
Scopus ID
85166345730
Erfassungsdatum
2024-01-08