@inproceedings{tong-etal-2025-pamn,
    title = "{PAMN}: Multi-phase Correlation Modeling for Contrast-Enhanced 3{D} Medical Image Retrieval",
    author = "Tong, Haonan  and
      Liu, Ke  and
      Zhang, Chuang  and
      Zhang, Xinglin  and
      Chen, Tao  and
      Hwang, Jenq-Neng  and
      Li, Lei",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.184/",
    doi = "10.18653/v1/2025.findings-emnlp.184",
    pages = "3456--3467",
    ISBN = "979-8-89176-335-7",
    abstract = "Contrast-enhanced 3D Medical imaging (e.g., CT, MRI) leverages phase sequences to uncover temporal dynamics vital for diagnosing tumors, lesions, and vascular issues. However, current retrieval models primarily focus on spatial features, neglecting phase-specific progression detailed in clinical reports. We present the **Phase-aware Memory Network (PAMN)**, a novel framework enhancing 3D medical image retrieval by fusing imaging phases with diagnostic text. PAMN creates rich radiological representations that enhance diagnostic accuracy by combining image details with clinical report context, rigorously tested on a novel phase-series dataset of 12,230 hospital CT scans. PAMN achieves an effective balance of performance and scalability in 3D radiology retrieval, outperforming state-of-the-art baselines through the robust fusion of spatial, temporal, and textual information."
}