Dai Lam Le, Minh Khoi Nguyen, Son Dinh Nguyen, Amir Reza Jafari, Viet Ha, Nguyen Son, Reza Farahbakhsh, Noël Crespi, Phi Le Nguyen · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.29905 · cited by 0 works (OpenAlex) · open-access PDF
Die automatisierte Röntgen-Verhaltensberichterstattung für Multi-Sequenz-Gehirn-MRI hat ein starkes Versprechen, die Arbeitsbelastung von Radiologen zu erleichtern, aber es bleibt ein anspruchsvolles Problem: Gehirn-MRI-Studien sind inhärent volumetrisch, jede Pulssequenz trägt ergänzende diagnostische Informationen, und annotierte Daten sind für klinische Sprachen mit geringen Ressourcen kritisch knapp. Die vorhandenen MRI-Berichterstattungsressourcen des Gehirns werden immer noch von englischen Berichten, übersetzten klinischen Texten oder Datensätzen dominiert, die von Aufgaben-spezifischen Imaging-Benchmarks angepasst werden, wodurch routinemäßige klinische Berichterstattung mit gering
Machine translation (Meta M2M100); the English original follows.
Automated radiology report generation for multi-sequence brain MRI holds strong promise for alleviating radiologists' workload, yet it remains a demanding problem: brain MRI studies are inherently volumetric, each pulse sequence carries complementary diagnostic information, and annotated data are critically scarce for low-resource clinical languages. Existing brain MRI-report resources are still dominated by English reports, translated clinical text, or datasets adapted from task-specific imaging benchmarks, leaving routine low-resource clinical reporting and heterogeneous multi-sequence MRI protocols insufficiently represented. To bridge this gap, we introduce ViBMR, to the best of our knowledge, the first large-scale multi-sequence brain MRI dataset paired with routine Vietnamese clinical reports, comprising 3,649 patients and 11,875 MRI series across T1, T2, FLAIR, and contrast-enhanced T1 sequences. Building on ViBMR, we establish a comprehensive evaluation setup for brain MRI report generation, covering representative medical vision-language models, multi-sequence inputs, and feature aggregation strategies. The results provide key insights into dataset characteristics and model behavior, while demonstrating the necessity of native low-resource clinical datasets for reliable Vietnamese-origin brain MRI report generation. Our dataset and baseline models will be released for non-commercial research.
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Topics: Medicine, Vietnamese, Neuroimaging, Medical physics, Computer science, Feature (linguistics)
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