Search Results for author: Pietro Mascagni

Found 18 papers, 8 papers with code

Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures

1 code implementation27 Jul 2023 Kun Yuan, Vinkle Srivastav, Tong Yu, Joel Lavanchy, Pietro Mascagni, Nassir Navab, Nicolas Padoy

SurgVLP constructs a new contrastive learning objective to align video clip embeddings with the corresponding multiple text embeddings by bringing them together within a joint latent space.

Automatic Speech Recognition Contrastive Learning +6

Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition

no code implementations21 Feb 2023 Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez, Tong Yu, Pietro Mascagni, Didier Mutter, Jacques Marescaux, Paolo Fiorini, Nicolas Padoy

In this work, we propose to use coarser and easier-to-annotate activity labels, namely phases, as weak supervision to learn step recognition with fewer step annotated videos.

Activity Recognition

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

no code implementations17 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.

Latent Graph Representations for Critical View of Safety Assessment

no code implementations8 Dec 2022 Aditya Murali, Deepak Alapatt, Pietro Mascagni, Armine Vardazaryan, Alain Garcia, Nariaki Okamoto, Didier Mutter, Nicolas Padoy

Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure.

Anatomy Image Reconstruction +1

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

no code implementations14 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

Live Laparoscopic Video Retrieval with Compressed Uncertainty

no code implementations8 Mar 2022 Tong Yu, Pietro Mascagni, Juan Verde, Jacques Marescaux, Didier Mutter, Nicolas Padoy

Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care.

Retrieval Video Retrieval

Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos

6 code implementations7 Sep 2021 Chinedu Innocent Nwoye, Tong Yu, Cristians Gonzalez, Barbara Seeliger, Pietro Mascagni, Didier Mutter, Jacques Marescaux, Nicolas Padoy

To achieve this task, we introduce our new model, the Rendezvous (RDV), which recognizes triplets directly from surgical videos by leveraging attention at two different levels.

Action Triplet Recognition

Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures

no code implementations24 Feb 2021 Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez, Tong Yu, Pietro Mascagni, Didier Mutter, Jacques Marescaux, Paolo Fiorini, Nicolas Padoy

Conclusion: In this work, we present a multi-task multi-stage temporal convolutional network for surgical activity recognition, which shows improved results compared to single-task models on the Bypass40 gastric bypass dataset with multi-level annotations.

Activity Recognition

Artificial Intelligence in Surgery: Neural Networks and Deep Learning

1 code implementation28 Sep 2020 Deepak Alapatt, Pietro Mascagni, Vinkle Srivastav, Nicolas Padoy

Deep neural networks power most recent successes of artificial intelligence, spanning from self-driving cars to computer aided diagnosis in radiology and pathology.

Self-Driving Cars

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