Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Srivastav, Vinkle"'
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
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
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:
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:
Yuan, Kun, Kattel, Manasi, Lavanchy, Joel L., Navab, Nassir, Srivastav, Vinkle, Padoy, Nicolas
Modern operating room is becoming increasingly complex, requiring innovative intra-operative support systems. While the focus of surgical data science has largely been on video analysis, integrating surgical computer vision with language capabilities
Externí odkaz:
http://arxiv.org/abs/2312.10251
Autor:
Alapatt, Deepak, Murali, Aditya, Srivastav, Vinkle, Mascagni, Pietro, Consortium, AI4SafeChole, Padoy, Nicolas
Purpose: General consensus amongst researchers and industry points to a lack of large, representative annotated datasets as the biggest obstacle to progress in the field of surgical data science. Self-supervised learning represents a solution to part
Externí odkaz:
http://arxiv.org/abs/2312.05968
Autor:
Yuan, Kun, Srivastav, Vinkle, Yu, Tong, Lavanchy, Joel L., Mascagni, Pietro, Navab, Nassir, Padoy, Nicolas
Recent advancements in surgical computer vision have been driven by vision-only models, which lack language semantics, relying on manually annotated videos to predict fixed object categories. This limits their generalizability to unseen surgical proc
Externí odkaz:
http://arxiv.org/abs/2307.15220
Autor:
Ramesh, Sanat, Srivastav, Vinkle, Alapatt, Deepak, Yu, Tong, Murali, Aditya, Sestini, Luca, Nwoye, Chinedu Innocent, Hamoud, Idris, Sharma, Saurav, Fleurentin, Antoine, Exarchakis, Georgios, Karargyris, Alexandros, Padoy, Nicolas
The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amount
Externí odkaz:
http://arxiv.org/abs/2207.00449
The fine-grained localization of clinicians in the operating room (OR) is a key component to design the new generation of OR support systems. Computer vision models for person pixel-based segmentation and body-keypoints detection are needed to better
Externí odkaz:
http://arxiv.org/abs/2108.11801
Publikováno v:
In Hashimoto D.A. (Ed.) Artificial Intelligence in Surgery: A Primer for Surgical Practice. New York: McGraw Hill. ISBN: 978-1260452730 (2020)
Deep neural networks power most recent successes of artificial intelligence, spanning from self-driving cars to computer aided diagnosis in radiology and pathology. The high-stake data intensive process of surgery could highly benefit from such compu
Externí odkaz:
http://arxiv.org/abs/2009.13411