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However, current vision-language models distort the intra-model relation and only include class information in reports that is insufficient for segmentation task. In this paper, we introduce a novel Bi-level class-severity-aware Vision-Language Graph Matching (Bi-VLGM) for text guided medical image segmentation, composed of a word-level VLGM module and a sentence-level VLGM module, to exploit the class-severity-aware relation among visual-textual features. In word-level VLGM, to mitigate the distorted intra-modal relation during VLM, we reformulate VLM as graph matching problem and introduce a vision-language graph matching (VLGM) to exploit the high-order relation among visual-textual features. Then, we perform VLGM between the local features for each class region and class-aware prompts to bridge their gap. In sentence-level VLGM, to provide disease severity information for segmentation task, we introduce a severity-aware prompting to quantify the severity level of disease lesion, and perform VLGM between the global features and the severity-aware prompts. By exploiting the relation between the local (global) and class (severity) features, the segmentation model can include the class-aware and severity-aware information to promote segmentation performance. Extensive experiments proved the effectiveness of our method and its superiority to existing methods. The source code will be released.<\/jats:p>","DOI":"10.1007\/s11263-024-02246-w","type":"journal-article","created":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T10:01:17Z","timestamp":1728208877000},"page":"1375-1391","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Bi-VLGM: Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation"],"prefix":"10.1007","volume":"133","author":[{"given":"Wenting","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0853-6948","authenticated-orcid":false,"given":"Yixuan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"issue":"2","key":"2246_CR1","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.sjopt.2011.01.009","volume":"25","author":"AA Alghadyan","year":"2011","unstructured":"Alghadyan, A. 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