@inproceedings{wu-etal-2026-mmclip,
    title = "{MMCLIP}: Cross-Modal Attention Masked Modelling for Medical Language-Image Pre-Training",
    author = "Wu, Biao  and
      Xie, Yutong  and
      Zhang, Zeyu  and
      Phan, Vu Minh Hieu  and
      Chen, Qi  and
      Chen, Ling  and
      Wu, Qi",
    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.113/",
    pages = "2440--2455",
    ISBN = "979-8-89176-390-6",
    abstract = "Vision-and-language pretraining (VLP) in medicine leverages contrastive learning on image{--}text pairs, often enhanced with masked modeling. However, existing methods face two challenges: difficulty reconstructing key pathological features due to limited data, and reliance on either paired or image-only datasets without combining both. To address this, we propose **MMCLIP** (**M**asked **M**edical **C**ontrastive **L**anguage{--}**I**mage **P**re-training), which introduces two modules: **AttMIM**: Masks image features highly correlated with text to improve reconstruction of fine medical details. **EntMLM**: Masks key medical entities in text and reconstructs them using visual cues. Furthermore, **MMCLIP** incorporates unpaired data through disease-kind prompts, achieving state-of-the-art performance in zero-shot and fine-tuning across five benchmarks."
}