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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
ChEX: Interactive Localization and Region Description in Chest X-Rays.
In: (18th European Conference on Computer Vision, ECCV 2024, 29 September - 4 October 2024, Milan). Berlin [u.a.]: Springer, 2025. 92-111 (Lect. Notes Comput. Sc. ; 15079 LNCS)
Report generation models offer fine-grained textual interpretations of medical images like chest X-rays, yet they often lack interactivity (i.e. the ability to steer the generation process through user queries) and localized interpretability (i.e. visually grounding their predictions), which we deem essential for future adoption in clinical practice. While there have been efforts to tackle these issues, they are either limited in their interactivity by not supporting textual queries or fail to also offer localized interpretability. Therefore, we propose a novel multitask architecture and training paradigm integrating textual prompts and bounding boxes for diverse aspects like anatomical regions and pathologies. We call this approach the Chest X-Ray Explainer (ChEX). Evaluations across a heterogeneous set of 9 chest X-ray tasks, including localized image interpretation and report generation, showcase its competitiveness with SOTA models while additional analysis demonstrates ChEX’s interactive capabilities. Code: https://github.com/philip-mueller/chex.
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Publikationstyp
Artikel: Konferenzbeitrag
Schlagwörter
Radiology Report Generation ; Vision-language Modeling
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Konferenztitel
18th European Conference on Computer Vision, ECCV 2024
Konferzenzdatum
29 September - 4 October 2024
Konferenzort
Milan
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 15079 LNCS,
Seiten: 92-111
Verlag
Springer
Verlagsort
Berlin [u.a.]
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)
Förderungen
German Academic Exchange Service (DAAD) under the Kondrad Zuse School of Excellence for Reliable AI (RelAI)
Bavarian Collaborative Research Project PRIPREKI of the Free State of Bavaria Funding Programme "Artificial Intelligence -Data Science"
Medical Informatics Initiative as part of the PrivateAIM Project
German Ministry of Education and Research
Bavarian State Ministry for Science and the Arts under the Munich Centre for Machine Learning (MCML)
German Federal Ministry of Education and Research
ERC Grant Deep4MI
Bavarian Collaborative Research Project PRIPREKI of the Free State of Bavaria Funding Programme "Artificial Intelligence -Data Science"
Medical Informatics Initiative as part of the PrivateAIM Project
German Ministry of Education and Research
Bavarian State Ministry for Science and the Arts under the Munich Centre for Machine Learning (MCML)
German Federal Ministry of Education and Research
ERC Grant Deep4MI