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ARXIV:2603.07839 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.07839MEDICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Leveraging pre-trained diffusion models for training-free object tracking in surgical videos, enabling real-time surgical assistance and analysis.
Opportunity summary
Pain Leveraging pre-trained diffusion models for training-free object tracking in surgical videos, enabling real-time surgical assistance and analysis.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Leveraging pre-trained diffusion models for training-free object tracking in surgical videos, enabling real-time surgical assistance and analysis. Our method addresses the challenges of costly pixel-level annotations and label inconsistencies inherent in existing datasets.
Purpose: In this paper, we present a novel approach for online object tracking in laparoscopic cholecystectomy (LC) surgical videos, targeting localisation and tracking of critical anatomical structures and instruments. Our method addresses the challenges…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results: Through a pilot study, we first demonstrate that diffusion features exhibit superior object localisation and consistent semantics across different decoder levels and temporal…
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Leveraging pre-trained diffusion models for training-free object tracking in surgical videos, enabling real-time surgical assistance and analysis.
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Paper Pack
10.48550/arXiv.2603.07839Leveraging pre-trained diffusion models for training-free object tracking in surgical videos, enabling real-time surgical assistance and analysis.
Abstract
Purpose: In this paper, we present a novel approach for online object tracking in laparoscopic cholecystectomy (LC) surgical videos, targeting localisation and tracking of critical anatomical structures and instruments. Our method addresses the challenges of costly pixel-level annotations and label inconsistencies inherent in existing datasets. Methods: Leveraging the inherent object localisation capabilities of pre-trained text-to-image diffusion models, we extract representative features from surgical frames without any training or fine-tuning. Our tracking framework uses these features, along with cross-frame interactions via an affinity matrix inspired by query-key-value attention, to ensure temporal continuity in the tracking process. Results: Through a pilot study, we first demonstrate that diffusion features exhibit superior object localisation and consistent semantics across different decoder levels and temporal frames. Later, we perform extensive experiments to validate the effectiveness of our approach, showcasing its superiority over competitors for the task of temporal object tracking. Specifically, we achieve a per-pixel classification accuracy of 79.19%, mean Jaccard Score of 56.20%, and mean F-Score of 79.48% on the publicly available CholeSeg8K dataset. Conclusion: Our work not only introduces a novel application of text-to-image diffusion models but also contributes to advancing the field of surgical video analysis, offering a promising avenue for accurate and cost-effective temporal object tracking in minimally invasive surgery videos.
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Extraction status
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 7.0
PROBLEM
Leveraging pre-trained diffusion models for training-free object tracking in surgical videos, enabling real-time surgical assistance and analysis. Our method addresses the challenges of costly pixel-level annotations and label inconsistencies inherent in existing datasets.
METHOD
Purpose: In this paper, we present a novel approach for online object tracking in laparoscopic cholecystectomy (LC) surgical videos, targeting localisation and tracking of critical anatomical structures and instruments. Our method addresses the challenges of costly pixel-level a...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results: Through a pilot study, we first demonstrate that diffusion features exhibit superior object localisation and consistent semantics across different decoder levels and temporal frames.
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Leveraging pre-trained diffusion models for training-free object tracking in surgical videos, enabling real-time surgical assistance and analysis. Our method addresses the challenges of costly pixel-level annotations and label inconsistencies inherent in existing datasets.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Purpose: In this paper, we present a novel approach for online object tracking in laparoscopic cholecystectomy (LC) surgical videos, targeting localisation and tracking of critical anatomical structures and instruments. Our method addresses the challenges of costly pixel-level annotations and label inconsistencies inherent in existing datasets.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results: Through a pilot study, we first demonstrate that diffusion features exhibit superior object localisation and consistent semantics across different decoder levels and temporal frames.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Leveraging pre-trained diffusion models for training-free object tracking in surgical videos, enabling real-time surgical assistance and analysis.
Segment
Medical AI
Adoption evidence
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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confidence low
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passport absent
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Technical feasibility
partial
Current read
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Run minimal reproduction from the Build Passport prototype path.
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Write integration checklist from prototype path and target workflow.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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