TiBiX: Leveraging Temporal Information for Bidirectional X-Ray and Report Generation
Sanjeev, Santosh Maani, Fadillah Adamsyah Abzhanov, Arsen Papineni, Vijay Ram Almakky, Ibrahim Papiez, Bartlomiej W. Yaqub, Mohammad
Sanjeev, Santosh
Maani, Fadillah Adamsyah
Abzhanov, Arsen
Papineni, Vijay Ram
Almakky, Ibrahim
Papiez, Bartlomiej W.
Yaqub, Mohammad
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
With the emergence of vision language models in the medical imaging domain, numerous studies have focused on two dominant research activities: (1) report generation from Chest X-rays (CXR), and (2) synthetic scan generation from text or reports. Despite some research incorporating multi-view CXRs into the generative process, prior patient scans and reports have been generally disregarded. This can inadvertently lead to the leaving out of important medical information, thus affecting generation quality. To address this, we propose TiBiX: Leveraging Temporal information for Bidirectional X-ray and Report Generation. Considering previous scans, our approach facilitates bidirectional generation, primarily addressing two challenging problems: (1) generating the current image from the previous image and current report and (2) generating the current report based on both the previous and current images. Moreover, we extract and release a curated temporal benchmark dataset derived from the MIMIC-CXR dataset, which focuses on temporal data. Our comprehensive experiments and ablation studies explore the merits of incorporating prior CXRs and achieve state-of-the-art (SOTA) results on the report generation task. Furthermore, we attain on-par performance with SOTA image generation efforts, thus serving as a new baseline in longitudinal bidirectional CXR-to-report generation. The code is available at https://github.com/BioMedIA-MBZUAI/TiBiX.
Citation
S. Sanjeev et al., “TiBiX: Leveraging Temporal Information for Bidirectional X-Ray and Report Generation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 15224 LNCS, pp. 169–179, 2025, doi: 10.1007/978-3-031-72744-3_17.
Source
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference
MICCAI Workshop on Deep Generative Models
Keywords
Bidirectional Generation, Chest X-ray generation, Longitudinal data, Multimodal Data, Report Generation
Subjects
Source
MICCAI Workshop on Deep Generative Models
Publisher
Springer Nature