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Talent Scout
C
Chiara Lena
Politecnico di Milano, Italy
C
Cesare Hassan
IRCCS Humanitas Research Hospital, Italy
D
Danail Stoyanov
University College London, UK
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Founder's Pitch
"Leveraging TemporalDoRA for robust surgical video QA in clinical settings, overcoming traditional model limitations."
Commercial Viability Breakdown
0-10 scaleHigh Potential
3/4 signals
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4/4 signals
Series A Potential
4/4 signals
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Why It Matters
Accurate interpretation of surgical videos is crucial for clinicians to make timely decisions during operations. Current models often fail with linguistic variation or transient events. TemporalDoRA enhances model robustness and accuracy by integrating temporal evidence effectively, potentially reducing surgical errors and improving patient outcomes.
Product Angle
This can be productized as a software tool integrated into existing surgical support systems, providing real-time video analysis and Q&A capabilities to improve decision-making during surgeries.
Disruption
This approach could replace traditional manual video monitoring and Q&A procedures during surgeries, moving from text-centric models to more visually and temporally grounded systems.
Product Opportunity
With the healthcare AI market rapidly growing, hospitals and surgical centers are likely to invest in technologies that enhance surgical accuracy and efficiency. Potentially a multi-billion dollar market, particularly valuable in minimally invasive surgery applications.
Use Case Idea
Develop an AI-assisted surgical tool that answers questions in real-time during procedures by analyzing live video feeds, aiding clinicians in making informed decisions quickly and accurately.
Science
TemporalDoRA modifies existing Parameter Efficient Fine-Tuning (PEFT) methods by incorporating temporal Multi-Head Attention within the low-rank adaptation pathway of video models. This allows for frame-level interactions that integrate sparse temporal evidence, enabling models to maintain robustness even with linguistic variability in questions.
Method & Eval
Tested on two datasets, REAL-Colon-VQA and EndoVis18-VQA, TemporalDoRA showed improved performance in handling linguistic variations and beat state-of-the-art models on several benchmarks, demonstrating its effectiveness and robustness in video-based question answering tasks.
Caveats
The system may struggle with novel procedures lacking adequate temporal data or where the visual cues are too sparse or ambiguous to provide reliable answers.
Author Intelligence
Luca Carlini
Politecnico di Milano, Italy
luca.carlini@polimi.it
Chiara Lena
Politecnico di Milano, Italy
Cesare Hassan
IRCCS Humanitas Research Hospital, Italy
Danail Stoyanov
University College London, UK
Elena De Momi
Politecnico di Milano, Italy
Sophia Bano
University College London, UK
Mobarak I. Hoque
University of Manchester, UK
mobarak.hoque@manchester.ac.uk