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ARXIV:2603.24539 · MEDICAL AI · SUBMITTED 26 MAR · 20:30 UTC · FRESHNESS STALE
ARXIV:2603.24539MEDICAL AISUBMITTED 26 MAR · 20:30 UTCFRESHNESS STALEFlorian Stilz · Vinkle Srivastav · Nassir Navab · Nicolas Padoy · arXiv
A pretraining framework for understanding long-form surgical videos, enabling zero-shot recognition of surgical events and improving multimodal alignment.
Opportunity summary
Pain A pretraining framework for understanding long-form surgical videos, enabling zero-shot recognition of surgical events and improving multimodal alignment.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence partial
A pretraining framework for understanding long-form surgical videos, enabling zero-shot recognition of surgical events and improving multimodal alignment. A particularly challenging area is the intraoperative surgical procedure domain, where labeled data is scarce, and…
Video-language foundation models have proven to be highly effective in zero-shot applications across a wide range of tasks. A particularly challenging area is the intraoperative surgical procedure domain, where labeled data is scarce, and…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our method is designed for fine-grained temporal video-text recognition and introduces several novel pretraining strategies to improve multimodal alignment in long-form surgical videos. A…
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A pretraining framework for understanding long-form surgical videos, enabling zero-shot recognition of surgical events and improving multimodal alignment.
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10.48550/arXiv.2603.24539A pretraining framework for understanding long-form surgical videos, enabling zero-shot recognition of surgical events and improving multimodal alignment.
Abstract
Video-language foundation models have proven to be highly effective in zero-shot applications across a wide range of tasks. A particularly challenging area is the intraoperative surgical procedure domain, where labeled data is scarce, and precise temporal understanding is often required for complex downstream tasks. To address this challenge, we introduce CliPPER (Contextual Video-Language Pretraining on Long-form Intraoperative Surgical Procedures for Event Recognition), a novel video-language pretraining framework trained on surgical lecture videos. Our method is designed for fine-grained temporal video-text recognition and introduces several novel pretraining strategies to improve multimodal alignment in long-form surgical videos. Specifically, we propose Contextual Video-Text Contrastive Learning (VTC_CTX) and Clip Order Prediction (COP) pretraining objectives, both of which leverage temporal and contextual dependencies to enhance local video understanding. In addition, we incorporate a Cycle-Consistency Alignment over video-text matches within the same surgical video to enforce bidirectional consistency and improve overall representation coherence. Moreover, we introduce a more refined alignment loss, Frame-Text Matching (FTM), to improve the alignment between video frames and text. As a result, our model establishes a new state-of-the-art across multiple public surgical benchmarks, including zero-shot recognition of phases, steps, instruments, and triplets. The source code and pretraining captions can be found at https://github.com/CAMMA-public/CliPPER.
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Proof status
partial0 refs; 0 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
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Dimensions overall score 7.0
PROBLEM
A pretraining framework for understanding long-form surgical videos, enabling zero-shot recognition of surgical events and improving multimodal alignment. A particularly challenging area is the intraoperative surgical procedure domain, where labeled data is scarce, and precise t...
METHOD
Video-language foundation models have proven to be highly effective in zero-shot applications across a wide range of tasks. A particularly challenging area is the intraoperative surgical procedure domain, where labeled data is scarce, and precise temporal understanding is often...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our method is designed for fine-grained temporal video-text recognition and introduces several novel pretraining strategies to improve multimodal alignment in long-form surgical videos. A public repositor...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A pretraining framework for understanding long-form surgical videos, enabling zero-shot recognition of surgical events and improving multimodal alignment. A particularly challenging area is the intraoperative surgical procedure domain, where labeled data is scarce, and precise temporal understanding is often required for complex downstream tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Video-language foundation models have proven to be highly effective in zero-shot applications across a wide range of tasks. A particularly challenging area is the intraoperative surgical procedure domain, where labeled data is scarce, and precise temporal understanding is often required for complex downstream tasks.
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. Our method is designed for fine-grained temporal video-text recognition and introduces several novel pretraining strategies to improve multimodal alignment in long-form surgical videos. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
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. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A pretraining framework for understanding long-form surgical videos, enabling zero-shot recognition of surgical events and improving multimodal alignment.
Segment
Medical AI
Adoption evidence
Public code linked for build inspection
Commercial read
7.0/10 public viability
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missing
reason
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proof status
unverified
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confidence low
next verification path
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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