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pro vyhledávání: '"Padoy, Nicolas"'
Accurate tool tracking is essential for the success of computer-assisted intervention. Previous efforts often modeled tool trajectories rigidly, overlooking the dynamic nature of surgical procedures, especially tracking scenarios like out-of-body and
Externí odkaz:
http://arxiv.org/abs/2405.20333
Natural language could play an important role in developing generalist surgical models by providing a broad source of supervision from raw texts. This flexible form of supervision can enable the model's transferability across datasets and tasks as na
Externí odkaz:
http://arxiv.org/abs/2405.10075
Purpose: In medical research, deep learning models rely on high-quality annotated data, a process often laborious and timeconsuming. This is particularly true for detection tasks where bounding box annotations are required. The need to adjust two cor
Externí odkaz:
http://arxiv.org/abs/2404.14344
We present a new self-supervised approach, SelfPose3d, for estimating 3d poses of multiple persons from multiple camera views. Unlike current state-of-the-art fully-supervised methods, our approach does not require any 2d or 3d ground-truth poses and
Externí odkaz:
http://arxiv.org/abs/2404.02041
We present a knowledge augmentation strategy for assessing the diagnostic groups and gait impairment from monocular gait videos. Based on a large-scale pre-trained Vision Language Model (VLM), our model learns and improves visual, textual, and numeri
Externí odkaz:
http://arxiv.org/abs/2403.13756
Autor:
Satyanaik, Siddhant, Murali, Aditya, Alapatt, Deepak, Wang, Xin, Mascagni, Pietro, Padoy, Nicolas
Purpose: Advances in deep learning have resulted in effective models for surgical video analysis; however, these models often fail to generalize across medical centers due to domain shift caused by variations in surgical workflow, camera setups, and
Externí odkaz:
http://arxiv.org/abs/2403.06953
Self-supervised learning (SSL) approaches have achieved great success when the amount of labeled data is limited. Within SSL, models learn robust feature representations by solving pretext tasks. One such pretext task is contrastive learning, which i
Externí odkaz:
http://arxiv.org/abs/2402.14611
Autor:
Murali, Aditya, Alapatt, Deepak, Mascagni, Pietro, Vardazaryan, Armine, Garcia, Alain, Okamoto, Nariaki, Costamagna, Guido, Mutter, Didier, Marescaux, Jacques, Dallemagne, Bernard, Padoy, Nicolas
This technical report provides a detailed overview of Endoscapes, a dataset of laparoscopic cholecystectomy (LC) videos with highly intricate annotations targeted at automated assessment of the Critical View of Safety (CVS). Endoscapes comprises 201
Externí odkaz:
http://arxiv.org/abs/2312.12429
Autor:
Hamoud, Idris, Jamal, Muhammad Abdullah, Srivastav, Vinkle, Mutter, Didier, Padoy, Nicolas, Mohareri, Omid
Surgical robotics holds much promise for improving patient safety and clinician experience in the Operating Room (OR). However, it also comes with new challenges, requiring strong team coordination and effective OR management. Automatic detection of
Externí odkaz:
http://arxiv.org/abs/2312.12250
Autor:
Lavanchy, Joel L., Ramesh, Sanat, Dall'Alba, Diego, Gonzalez, Cristians, Fiorini, Paolo, Muller-Stich, Beat, Nett, Philipp C., Marescaux, Jacques, Mutter, Didier, Padoy, Nicolas
Most studies on surgical activity recognition utilizing Artificial intelligence (AI) have focused mainly on recognizing one type of activity from small and mono-centric surgical video datasets. It remains speculative whether those models would genera
Externí odkaz:
http://arxiv.org/abs/2312.11250