Search Results for author: Sebastian Bodenstedt

Found 16 papers, 6 papers with code

Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration

no code implementations26 Feb 2020 Dominik Rivoir, Sebastian Bodenstedt, Felix von Bechtolsheim, Marius Distler, Jürgen Weitz, Stefanie Speidel

Using our unsupervised method as an auxiliary task for RSD training, we outperform other self-supervised methods and are comparable to the supervised state-of-the-art.

Auxiliary Learning Video Segmentation +1

Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video

1 code implementation26 Jul 2019 Isabel Funke, Sebastian Bodenstedt, Florian Oehme, Felix von Bechtolsheim, Jürgen Weitz, Stefanie Speidel

However, surgical gesture recognition based only on video is a challenging problem that requires effective means to extract both visual and temporal information from the video.

Gesture Recognition Surgical Gesture Recognition

2017 Robotic Instrument Segmentation Challenge

3 code implementations18 Feb 2019 Max Allan, Alex Shvets, Thomas Kurmann, Zichen Zhang, Rahul Duggal, Yun-Hsuan Su, Nicola Rieke, Iro Laina, Niveditha Kalavakonda, Sebastian Bodenstedt, Luis Herrera, Wenqi Li, Vladimir Iglovikov, Huoling Luo, Jian Yang, Danail Stoyanov, Lena Maier-Hein, Stefanie Speidel, Mahdi Azizian

In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison.

Person Re-Identification 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

Unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis

no code implementations13 Feb 2017 Sebastian Bodenstedt, Martin Wagner, Darko Katić, Patrick Mietkowski, Benjamin Mayer, Hannes Kenngott, Beat Müller-Stich, Rüdiger Dillmann, Stefanie Speidel

In this paper, we address this problem by presenting an unsupervised method for training a convolutional neural network (CNN) to differentiate between laparoscopic video frames on a temporal basis.

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