Jumpstarting Surgical Computer Vision

no code implementations • 10 Dec 2023 • Deepak Alapatt, Aditya Murali, Vinkle Srivastav, Pietro Mascagni, AI4SafeChole Consortium, Nicolas Padoy

Methods: In this work, we employ self-supervised learning to flexibly leverage diverse surgical datasets, thereby learning taskagnostic representations that can be used for various surgical downstream tasks.

Self-Supervised Learning Transfer Learning

Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videos

no code implementations • 17 Jan 2023 • Joël L. Lavanchy, Armine Vardazaryan, Pietro Mascagni, AI4SafeChole Consortium, Didier Mutter, Nicolas Padoy

Results: The internal dataset consisting of 356, 267 images from 48 videos and the two multicentric test datasets consisting of 54, 385 and 58, 349 images from 10 and 20 videos, respectively, were annotated.

Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

no code implementations • 14 Mar 2022 • Hasan Kassem, Deepak Alapatt, Pietro Mascagni, AI4SafeChole Consortium, Alexandros Karargyris, Nicolas Padoy

With these constraints in mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos, thereby improving performance on the task of surgical phase recognition.

Federated Learning Self-Supervised Learning +1