@inproceedings{nguyen-etal-2026-region,
    title = "Region-Grounded Report Generation for 3{D} Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework",
    author = "Nguyen, Cong Huy  and
      Nguyen, Son Dinh  and
      Li, Guanlin  and
      Nguyen, Tuan Dung  and
      Sankaran, Aditya Narayan  and
      Thong, Mai Huy  and
      Nguyen, Thanh Trung  and
      Son, Mai Hong  and
      Farahbakhsh, Reza  and
      Nguyen, Phi Le  and
      Crespi, Noel",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.903/",
    pages = "19725--19740",
    ISBN = "979-8-89176-390-6",
    abstract = "Automated medical report generation for 3D PET/CT imaging is fundamentally challenged by the high-dimensional nature of volumetric data and a critical scarcity of annotated datasets, particularly for low-resource languages. Current black-box methods map whole volumes to reports, ignoring the clinical workflow of analyzing localized Regions of Interest (RoIs) to derive diagnostic conclusions. In this paper, we bridge this gap by introducing VietPET-RoI, the first large-scale 3D PET/CT dataset with fine-grained RoI annotation for a low-resource language, comprising 600 PET/CT samples and 1,960 manually annotated RoIs, paired with corresponding clinical reports. Furthermore, to demonstrate the utility of this dataset, we propose HiRRA, a novel framework that mimics the professional radiologist diagnostic workflow by employing graph-based relational modules to capture dependencies between RoI attributes. This approach shifts from global pattern matching toward localized clinical findings. Additionally, we introduce new clinical evaluation metrics, namely RoI Coverage and RoI Quality Index, that measure both RoI localization accuracy and attribute description fidelity using LLM-based extraction. Extensive evaluation demonstrates that our framework achieves SOTA performance, surpassing existing models by 19.7{\%} in BLEU and 4.7{\%} in ROUGE-L, while achieving a remarkable 45.8{\%} improvement in clinical metrics, indicating enhanced clinical reliability and reduced hallucination. Our code and dataset are available on GitHub."
}