bibtext: @InProceedings{ Li_Anatomical_MICCAI2024,
                   author = { Li, Qingqiu and Yan, Xiaohan and Xu, Jilan and Yuan, Runtian and Zhang, Yuejie and Feng, Rui and Shen, Quanli and Zhang, Xiaobo and Wang, Shujun },
                   title = { { Anatomical Structure-Guided Medical Vision-Language Pre-training } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15011 },
                   month = {October},
                   pages = { pending },
                }
abstract: Learning medical visual representations through vision-language pre-training has reached remarkable progress. Despite the promising performance, it still faces challenges, i.e., local alignment lacks interpretability and clinical relevance, and the insufficient internal and external representation learning of image-report pairs. To address these issues, we propose an Anatomical Structure-Guided (ASG) framework. Specifically, we parse raw reports into triplets <anatomical region, finding, existence>, and fully utilize each element as supervision to enhance representation learning. For anatomical region, we design an automatic anatomical region-sentence alignment paradigm in collaboration with radiologists, considering them as the minimum semantic units to explore fine-grained local alignment. For finding and existence, we regard them as image tags, applying an image-tag recognition decoder to associate image features with their respective tags within each sample and constructing soft labels for contrastive learning to improve the semantic association of different image-report pairs. We evaluate the proposed ASG framework on two downstream tasks, including five public benchmarks. Experimental results demonstrate that our method outperforms the state-of-the-art methods. Our code is available at https://asgmvlp.github.io.
title: Anatomical Structure-Guided Medical Vision-Language Pre-training
authors: Li, Qingqiu Yan, Xiaohan Xu, Jilan Yuan, Runtian Zhang, Yuejie Feng, Rui Shen, Quanli Zhang, Xiaobo Wang, Shujun