Search Results for author: Johannes Paetzold

Found 10 papers, 5 papers with code

Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation

1 code implementation14 Aug 2019 Fernando Navarro, Suprosanna Shit, Ivan Ezhov, Johannes Paetzold, Andrei Gafita, Jan Peeken, Stephanie Combs, Bjoern Menze

Multi-organ segmentation in whole-body computed tomography (CT) is a constant pre-processing step which finds its application in organ-specific image retrieval, radiotherapy planning, and interventional image analysis.

Computed Tomography (CT) Image Retrieval +3

A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images

no code implementations24 Oct 2021 Giles Tetteh, Fernando Navarro, Johannes Paetzold, Jan Kirschke, Claus Zimmer, Bjoern H. Menze

First, it is time-consuming - the clinician needs to scan through several slices of images to ascertain the region of interest before deciding on what severity grade to assign to a patient.

Denoising

Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

1 code implementation7 Nov 2021 Ivan Ezhov, Kevin Scibilia, Katharina Franitza, Felix Steinbauer, Suprosanna Shit, Lucas Zimmer, Jana Lipkova, Florian Kofler, Johannes Paetzold, Luca Canalini, Diana Waldmannstetter, Martin Menten, Marie Metz, Benedikt Wiestler, Bjoern Menze

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration.

MORPH

Relationformer: A Unified Framework for Image-to-Graph Generation

1 code implementation19 Mar 2022 Suprosanna Shit, Rajat Koner, Bastian Wittmann, Johannes Paetzold, Ivan Ezhov, Hongwei Li, Jiazhen Pan, Sahand Sharifzadeh, Georgios Kaissis, Volker Tresp, Bjoern Menze

We leverage direct set-based object prediction and incorporate the interaction among the objects to learn an object-relation representation jointly.

Graph Generation Object +4

Unsupervised Anomaly Localization with Structural Feature-Autoencoders

1 code implementation23 Aug 2022 Felix Meissen, Johannes Paetzold, Georgios Kaissis, Daniel Rueckert

Most commonly, the anomaly detection model generates a "normal" version of an input image, and the pixel-wise $l^p$-difference of the two is used to localize anomalies.

Unsupervised Anomaly Detection

A Domain-specific Perceptual Metric via Contrastive Self-supervised Representation: Applications on Natural and Medical Images

no code implementations3 Dec 2022 Hongwei Bran Li, Chinmay Prabhakar, Suprosanna Shit, Johannes Paetzold, Tamaz Amiranashvili, JianGuo Zhang, Daniel Rueckert, Juan Eugenio Iglesias, Benedikt Wiestler, Bjoern Menze

We find that in the natural image domain, CSR behaves on par with the supervised one on several perceptual tests as a metric, and in the medical domain, CSR better quantifies perceptual similarity concerning the experts' ratings.

Image Generation

Surface Normal Estimation with Transformers

no code implementations11 Jan 2024 Barry Shichen Hu, Siyun Liang, Johannes Paetzold, Huy H. Nguyen, Isao Echizen, Jiapeng Tang

To avoid these limitations, we first unify the design choices in previous works and then propose a simplified Transformer-based model to extract richer and more robust geometric features for the surface normal estimation task.

Surface Normal Estimation

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