PuSH - Publication Server of Helmholtz Zentrum München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
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.
Scopus
Cited By
Altmetric
3
Tags
Annotations
Special Publikation
Hide on homepage

Edit extra information
Edit own tags
Private
Edit own annotation
Private
Hide on publication lists
on hompage
Mark as special
publikation
Publication type Article: Conference contribution
Keywords Radiology Report Generation ; Vision-language Modeling
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Conference Title 18th European Conference on Computer Vision, ECCV 2024
Conference Date 29 September - 4 October 2024
Conference Location Milan
Journal Lecture Notes in Computer Science
Quellenangaben Volume: 15079 LNCS, Issue: , Pages: 92-111 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]
Institute(s) 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-507100-001
DOI 10.1007/978-3-031-72664-4_6
Scopus ID 105018202122
Erfassungsdatum 2025-10-23
[➜Report correction]