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

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)
DOI
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
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
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, Heft: , Seiten: 92-111 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) 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-507100-001
DOI 10.1007/978-3-031-72664-4_6
Scopus ID 105018202122
Erfassungsdatum 2025-10-23
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