Search Results for author: Rickmer Braren

Found 17 papers, 8 papers with code

Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients

no code implementations8 Nov 2022 Alexander Ziller, Ayhan Can Erdur, Friederike Jungmann, Daniel Rueckert, Rickmer Braren, Georgios Kaissis

The prediction of pancreatic ductal adenocarcinoma therapy response is a clinically challenging and important task in this high-mortality tumour entity.

Representation Learning

U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction

no code implementations29 Jul 2021 Matthias Keicher, Hendrik Burwinkel, David Bani-Harouni, Magdalini Paschali, Tobias Czempiel, Egon Burian, Marcus R. Makowski, Rickmer Braren, Nassir Navab, Thomas Wendler

Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality.

Decision Making Graph Attention

Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation

no code implementations9 Jul 2021 Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kritika Prakash, Andrew Trask, Rickmer Braren, Marcus Makowski, Daniel Rueckert, Georgios Kaissis

Reconciling large-scale ML with the closed-form reasoning required for the principled analysis of individual privacy loss requires the introduction of new tools for automatic sensitivity analysis and for tracking an individual's data and their features through the flow of computation.

BIG-bench Machine Learning

Differentially private federated deep learning for multi-site medical image segmentation

1 code implementation6 Jul 2021 Alexander Ziller, Dmitrii Usynin, Nicolas Remerscheid, Moritz Knolle, Marcus Makowski, Rickmer Braren, Daniel Rueckert, Georgios Kaissis

The application of PTs to FL in medical imaging and the trade-offs between privacy guarantees and model utility, the ramifications on training performance and the susceptibility of the final models to attacks have not yet been conclusively investigated.

Federated Learning Image Segmentation +3

Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs

1 code implementation12 Mar 2021 Seong Tae Kim, Leili Goli, Magdalini Paschali, Ashkan Khakzar, Matthias Keicher, Tobias Czempiel, Egon Burian, Rickmer Braren, Nassir Navab, Thomas Wendler

Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation.

Computed Tomography (CT) COVID-19 Image Segmentation +2

Efficient, high-performance pancreatic segmentation using multi-scale feature extraction

1 code implementation2 Sep 2020 Moritz Knolle, Georgios Kaissis, Friederike Jungmann, Sebastian Ziegelmayer, Daniel Sasse, Marcus Makowski, Daniel Rueckert, Rickmer Braren

For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial.

The Liver Tumor Segmentation Benchmark (LiTS)

6 code implementations13 Jan 2019 Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

Benchmarking Computed Tomography (CT) +2

Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks

no code implementations4 Jun 2018 Hongwei Li, Kanru Lin, Maximilian Reichert, Lina Xu, Rickmer Braren, Deliang Fu, Roland Schmid, Ji Li, Bjoern Menze, Kuangyu Shi

The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early differential diagnosis of pancreatic cysts, which are identified in up to 16% of normal subjects, and some of which may develop into PDAC.

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