TiBiX: Leveraging Temporal Information for Bidirectional X-ray and
  Report Generation
Santosh Sanjeev Fadillah Adamsyah Maani Arsen Abzhanov Vijay Ram Papineni Ibrahim Almakky Bartłomiej W. Papież Mohammad Yaqub
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.