Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation
Surgical instrument segmentation (SIS) is fundamental for computer-assisted surgery, where reliable instrument masks enable precise scene understanding and clinical assistance. Recently, adapting foundation models like the Segment Anything Model (SAM) to the surgical domain via prompt-learning has shown encouraging results. However, the performance of these adapted models under challenging surgical conditions is constrained by suboptimal adaptation mechanisms. Specifically, optimizing prompts or prototypes purely via downstream segmentation loss tends to cause them to degenerate into task-spec
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzyOverlapping authors or contributors · 78%sgl-project/sglang →
“Shared author/contributor keys: luo, zhou”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%shareAI-lab/learn-claude-code →
“Shared author/contributor keys: yue”
- LinkedLinked via arxiv author · 85%Xinning Yao →
“Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation”
- LinkedLinked via arxiv author · 85%Jingjing Wang →
“Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation”
- LinkedLinked via arxiv author · 85%Jinghua Yue →
“Hierarchical Prototype-Memory Adaptation of SAM for Surgical Instrument Segmentation”