5 papers
SurgViVQA: Temporally-Grounded Video Question Answering for Surgical Scene Understanding
Mauro Orazio Drago, Luca Carlini, Pelinsu Celebi Balyemez +7
Video Question Answering (VideoQA) in the surgical domain aims to enhance intraoperative understanding by enabling AI models to reason over temporally coherent events rather than i…
When to Trust the Answer: Question-Aligned Semantic Nearest Neighbor Entropy for Safer Surgical VQA
Luca Carlini, Dennis Pierantozzi, Mauro Orazio Drago +6
Safety and reliability are critical for deploying visual question answering (VQA) systems in surgery, where incorrect or ambiguous responses can cause patient harm. A key limitatio…
TemporalDoRA: Temporal PEFT for Robust Surgical Video Question Answering
Luca Carlini, Chiara Lena, Cesare Hassan +4
Surgical Video Question Answering (VideoQA) requires accurate temporal grounding while remaining robust to natural variation in how clinicians phrase questions, where linguistic bi…
RealSynCol: a high-fidelity synthetic colon dataset for 3D reconstruction applications
Chiara Lena, Davide Milesi, Alessandro Casella +10
Deep learning has the potential to improve colonoscopy by enabling 3D reconstruction of the colon, providing a comprehensive view of mucosal surfaces and lesions, and facilitating…
DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation Model
Mona Sheikh Zeinoddin, Chiara Lena, Jiongqi Qu +11
Robotic-assisted surgery (RAS) relies on accurate depth estimation for 3D reconstruction and visualization. While foundation models like Depth Anything Models (DAM) show promise, d…