Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation
Text-guided medical image segmentation leverages clinical semantics to improve lesion delineation, yet many existing models bind cross-modal fusion, supervision, and decoder design into a task-specific architecture. Such tight coupling makes it difficult to reuse language guidance modules across heterogeneous vision and text backbones, and often requires redesigning the network when the encoder pair changes. This paper presents BTHA, a backbone-transferable hierarchical adapter framework for text-guided medical image segmentation. BTHA is built around a stable feature-level interface: given mu
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- LinkedLinked via arxiv author · 85%Yungeng Liu →
“Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation”
- LinkedLinked via arxiv author · 85%Xuanzi Fang →
“Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation”
- LinkedLinked via arxiv author · 85%Haijin Zeng →
“Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation”
- LinkedLinked via arxiv author · 85%Qi Dai →
“Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation”
- LinkedLinked via arxiv author · 85%Yongyong Chen →
“Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation”