Search Results for author: Stefanie Speidel

Found 34 papers, 16 papers with code

One model to use them all: Training a segmentation model with complementary datasets

1 code implementation29 Feb 2024 Alexander C. Jenke, Sebastian Bodenstedt, Fiona R. Kolbinger, Marius Distler, Jürgen Weitz, Stefanie Speidel

In this work, we propose a method to combine multiple partially annotated datasets, which provide complementary annotations, into one model, enabling better scene segmentation and the use of multiple readily available datasets.

Anatomy Scene Segmentation +1

Graph data modelling for outcome prediction in oropharyngeal cancer patients

no code implementations4 Oct 2023 Nithya Bhasker, Stefan Leger, Alexander Zwanenburg, Chethan Babu Reddy, Sebastian Bodenstedt, Steffen Löck, Stefanie Speidel

Graph neural networks (GNNs) are becoming increasingly popular in the medical domain for the tasks of disease classification and outcome prediction.

Computed Tomography (CT)

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

1 code implementation19 Jul 2023 Isabel Funke, Dominik Rivoir, Stefanie Krell, Stefanie Speidel

In addition, we propose to train the feature extractor, a standard CNN, together with an LSTM on preferably long video segments, i. e., with long temporal context.

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

Non-rigid Point Cloud Registration for Middle Ear Diagnostics with Endoscopic Optical Coherence Tomography

1 code implementation26 Apr 2023 Peng Liu, Jonas Golde, Joseph Morgenstern, Sebastian Bodenstedt, Chenpan Li, Yujia Hu, Zhaoyu Chen, Edmund Koch, Marcus Neudert, Stefanie Speidel

To overcome the lack of labeled training data, a fast and effective generation pipeline in Blender3D is designed to simulate middle ear shapes and extract in-vivo noisy and partial point clouds.

Point Cloud Registration

Why is the winner the best?

no code implementations CVPR 2023 Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, Ali Emre Kavur, Oldřich Kodym, Michal Kozubek, Jianning Li, Hongwei Li, Jun Ma, Carlos Martín-Isla, Bjoern Menze, Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patiño, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin Van Wijnen, Martin Wagner, Donglai Wei, Amine Yamlahi, Moi Hoon Yap, Chun Yuan, Maximilian Zenk, Aneeq Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, Jihoon Cho, Chanyeol Choi, Qi Dou, Ivan Ezhov, Christoph M. Friedrich, Clifton Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García Faura, Maria Grammatikopoulou, SeulGi Hong, Mostafa Jahanifar, Ikbeom Jang, Abdolrahim Kadkhodamohammadi, Inha Kang, Florian Kofler, Satoshi Kondo, Hugo Kuijf, Mingxing Li, Minh Huan Luu, Tomaž Martinčič, Pedro Morais, Mohamed A. Naser, Bruno Oliveira, David Owen, Subeen Pang, Jinah Park, Sung-Hong Park, Szymon Płotka, Elodie Puybareau, Nasir Rajpoot, Kanghyun Ryu, Numan Saeed, Adam Shephard, Pengcheng Shi, Dejan Štepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Helene Urien, João L. Vilaça, Kareem Abdul Wahid, Haojie Wang, Jiacheng Wang, Liansheng Wang, Xiyue Wang, Benedikt Wiestler, Marek Wodzinski, Fangfang Xia, Juanying Xie, Zhiwei Xiong, Sen yang, Yanwu Yang, Zixuan Zhao, Klaus Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein

The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.

Benchmarking Multi-Task Learning

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

SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction

1 code implementation22 Dec 2020 P. J. "Eddie'' Edwards, Dimitris Psychogyios, Stefanie Speidel, Lena Maier-Hein, Danail Stoyanov

The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths with coverage over the majority of the endoscopic images.

3D Reconstruction Anatomy

Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model

1 code implementation29 May 2020 Micha Pfeiffer, Carina Riediger, Stefan Leger, Jens-Peter Kühn, Danilo Seppelt, Ralf-Thorsten Hoffmann, Jürgen Weitz, Stefanie Speidel

In this work, we train a convolutional neural network to perform both the search for surface correspondences as well as the non-rigid registration in one step.

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.

Benchmarking Person Re-Identification +2

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

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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