Search Results for author: Dominik Rivoir

Found 8 papers, 5 papers with code

Exploring Semantic Consistency in Unpaired Image Translation to Generate Data for Surgical Applications

1 code implementation6 Sep 2023 Danush Kumar Venkatesh, Dominik Rivoir, Micha Pfeiffer, Fiona Kolbinger, Marius Distler, Jürgen Weitz, Stefanie Speidel

This study empirically investigates unpaired image translation methods for generating suitable data in surgical applications, explicitly focusing on semantic consistency.

Contrastive Learning Image-to-Image Translation +2

TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognition

no code implementations19 Jul 2023 Isabel Funke, Dominik Rivoir, Stefanie Krell, Stefanie Speidel

To enable context-aware computer assistance in the operating room of the future, cognitive systems need to understand automatically which surgical phase is being performed by the medical team.

Surgical phase recognition

Metrics Matter in Surgical Phase Recognition

1 code implementation23 May 2023 Isabel Funke, Dominik Rivoir, Stefanie Speidel

Surgical phase recognition is a basic component for different context-aware applications in computer- and robot-assisted surgery.

Surgical phase recognition

On the Pitfalls of Batch Normalization for End-to-End Video Learning: A Study on Surgical Workflow Analysis

1 code implementation15 Mar 2022 Dominik Rivoir, Isabel Funke, Stefanie Speidel

In this paper, we analyze pitfalls of BN in video learning, including issues specific to online tasks such as a 'cheating' effect in anticipation.

Video Understanding

Long-Term Temporally Consistent Unpaired Video Translation from Simulated Surgical 3D Data

1 code implementation ICCV 2021 Dominik Rivoir, Micha Pfeiffer, Reuben Docea, Fiona Kolbinger, Carina Riediger, Jürgen Weitz, Stefanie Speidel

However for transfer from simulated to photorealistic sequences, available information on the underlying geometry offers potential for achieving global consistency across views.

Neural Rendering Translation

Active Learning using Deep Bayesian Networks for Surgical Workflow Analysis

no code implementations8 Nov 2018 Sebastian Bodenstedt, Dominik Rivoir, Alexander Jenke, Martin Wagner, Michael Breucha, Beat Müller-Stich, Sören Torge Mees, Jürgen Weitz, Stefanie Speidel

For many applications in the field of computer assisted surgery, such as providing the position of a tumor, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery, methods for surgical workflow analysis are a prerequisite.

Active Learning BIG-bench Machine Learning

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