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Tanida, T.* ; Müller, P.* ; Kaissis, G. ; Rueckert, D.*

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)
DOI
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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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Cell Microscopy ; Medical And Biological Vision
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1063-6919
Konferenztitel IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Konferzenzdatum 17-24 June 2023
Konferenzort Vancouver, BC, Canada
Quellenangaben Band: 2023-June, Heft: , Seiten: 7433-7442 Artikelnummer: , Supplement: ,
Verlag Ieee Computer Soc
Verlagsort 10662 Los Vaqueros Circle, Po Box 3014, Los Alamitos, Ca 90720-1264 Usa
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-530014-001
G-507100-001
DOI 10.1109/CVPR52729.2023.00718
WOS ID 001058542607076
Scopus ID 85166345730
Erfassungsdatum 2024-01-08
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