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ARXIV:2605.13530 · MEDICAL AI · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.13530MEDICAL AISUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHJincai Huang · Shihao Zou · Yuchen Guo · Jingjing Li · Wei Ji · Kai Wang · +2 at arXiv
A unified framework for surgical scene understanding that combines high-level reasoning with low-level visual grounding using multimodal LLMs, improving phase recognition and instrument segmentation.
Opportunity summary
Pain A unified framework for surgical scene understanding that combines high-level reasoning with low-level visual grounding using multimodal LLMs, improving phase recognition and instrument segmentation.
Evidence 0 refs | 0 sources | 0% coverage
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A unified framework for surgical scene understanding that combines high-level reasoning with low-level visual grounding using multimodal LLMs, improving phase recognition and instrument segmentation. While recent advances, particularly in surgical image segmentation, have driven…
Surgical scene understanding is a cornerstone of computer-assisted intervention. While recent advances, particularly in surgical image segmentation, have driven progress, real-world clinical applications require a more holistic understanding that jointly captures procedural context, semantic…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Given surgical videos, SurgMLLM fine-tunes a multimodal large language model (MLLM) to support structured interpretability reasoning, which is used to jointly model phases, instrument-verb-target…
Medical AI moved forward this cycle; last verified May 2026. Public score 8.0/10. Production flags indicate code availability.
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A unified framework for surgical scene understanding that combines high-level reasoning with low-level visual grounding using multimodal LLMs, improving phase recognition and instrument segmentation.
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10.48550/arXiv.2605.13530A unified framework for surgical scene understanding that combines high-level reasoning with low-level visual grounding using multimodal LLMs, improving phase recognition and instrument segmentation.
Abstract
Surgical scene understanding is a cornerstone of computer-assisted intervention. While recent advances, particularly in surgical image segmentation, have driven progress, real-world clinical applications require a more holistic understanding that jointly captures procedural context, semantic reasoning, and precise visual grounding. However, existing approaches typically address these components in isolation, leading to fragmented representations and limited semantic consistency. To address this limitation, we propose SurgMLLM, a unified surgical scene understanding framework that bridges high-level reasoning and low-level visual grounding within a single model. Given surgical videos, SurgMLLM fine-tunes a multimodal large language model (MLLM) to support structured interpretability reasoning, which is used to jointly model phases, instrument-verb-target (IVT) triplets, and triplet-entity segmentation tokens. These tokens are then temporally aggregated and serve as prompts for a segmentation network, enabling accurate pixel-wise grounding of triplet instruments and targets. The entire framework is trained end-to-end with a unified objective that couples language-based reasoning supervision with visual grounding losses, promoting coherent cross-task learning and clinically consistent scene representations. To facilitate unified evaluation, we introduce CholecT45-Scene, extending CholecT45 dataset with 64,299 frames of pixel-level mask annotations for instruments and targets, aligned with existing triplet labels. Extensive experiments show that SurgMLLM significantly advances surgical scene understanding, improving the primary triplet recognition metric AP_IVT from 40.7% to 46.0% and consistently outperforming prior methods in phase recognition and segmentation. These results highlight the effectiveness of unified reasoning-and-grounding for reliable, context-aware surgical assistance.
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PROBLEM
A unified framework for surgical scene understanding that combines high-level reasoning with low-level visual grounding using multimodal LLMs, improving phase recognition and instrument segmentation. While recent advances, particularly in surgical image segmentation, have driven...
METHOD
Surgical scene understanding is a cornerstone of computer-assisted intervention. While recent advances, particularly in surgical image segmentation, have driven progress, real-world clinical applications require a more holistic understanding that jointly captures procedural cont...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Given surgical videos, SurgMLLM fine-tunes a multimodal large language model (MLLM) to support structured interpretability reasoning, which is used to jointly model phases, instrument-verb-target (IVT) tr...
WHY NOW
Medical AI moved forward this cycle; last verified May 2026. Public score 8.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A unified framework for surgical scene understanding that combines high-level reasoning with low-level visual grounding using multimodal LLMs, improving phase recognition and instrument segmentation. While recent advances, particularly in surgical image segmentation, have driven progress, real-world clinical applications require a more holistic understanding that jointly captures procedural context, semantic reasoning, and precise visual grounding.
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partial
Surgical scene understanding is a cornerstone of computer-assisted intervention. While recent advances, particularly in surgical image segmentation, have driven progress, real-world clinical applications require a more holistic understanding that jointly captures procedural context, semantic reasoning, and precise visual grounding.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Given surgical videos, SurgMLLM fine-tunes a multimodal large language model (MLLM) to support structured interpretability reasoning, which is used to jointly model phases, instrument-verb-target (IVT) triplets, and triplet-entity segmentation tokens. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified May 2026. Public score 8.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A unified framework for surgical scene understanding that combines high-level reasoning with low-level visual grounding using multimodal LLMs, improving phase recognition and instrument segmentation.
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