Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation

[2607.16327] Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation Skip to main content Search Submit Donate Log in Computer Science > Computer Vision and Pattern Recognition arXiv:2607.16327 (cs) [Submitted on 15 Jul 2026 ( v1 ), last revised 22 Aug 2026 (this version, v2)] Title: Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation Authors: Songyue Han , Mingye Zou , Shuchang Ye , Lei Bi , Mingyuan Meng View PDF HTML (experimental) Abstract: Medical image segmentation is essential for modern computer-aided medicine. Recently, text-guided segmentation has shown promise by incorporating clinician-formulated textual reports as semantic guidance for image segmentation. These textual reports contain language descriptions about the appearance, location, and neighboring anatomy of segmentation targets, providing explicit guidance for target localization and delineation. Existing text-guided segmentation methods typically extract textual semantics implicitly through a pretrained text encoder and then integrate vision-language semantics via straightforward image-text feature fusion. However, these methods do not explicitly capture target-oriented information embedded in textual reports, particularly target location, and do not explore multi-level information fusion strategies beyond basic feature-level fusion, limiting the extraction and integration of critical textual semantics. In this study, we propose LoG, a localization-infused vision-language fusion framework for text-guided medical image segmentation. By jointly performing multi-scale target localization tasks, LoG explicitly captures target-oriented vision-language semantics and enables three-level localization-infused semantic fusion: (i) localization-guided feature fusion that directly infuses location-relevant semantics into visual features, (ii) localization-gated attention fusion that redirects multi-scale localization predictions to reinforce critical regions, and (iii) localization-constrained loss fusion that supervises segmentation based on spatial consistency with target localization. Extensive experiments on three well-established benchmark datasets, involving three medical imaging modalities with paired textual reports, demonstrate that LoG consistently outperforms state-of-the-art medical image segmentation methods. Comments: 12 pages, 6 figures, 7 tables. v2: revised presentation with updated author affiliations; abstract condensed; Figure 1 redrawn; an average performance column added to Table I; one reference added and three removed; author biographies removed. All datasets, methods and experimental results are unchanged from v1 Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2607.16327 [cs.CV] (or arXiv:2607.16327v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.16327 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Songyue Han [ view email ] [v1] Wed, 15 Jul 2026 14:06:17 UTC (8,056 KB) [v2] Sat, 22 Aug 2026 14:17:42 UTC (3,027 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CV < prev | next > new | recent | 2026-07 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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