Search Results for author: Christian Payer

Found 8 papers, 4 papers with code

Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with Low GPU Memory Requirements

1 code implementation26 Nov 2021 Franz Thaler, Christian Payer, Horst Bischof, Darko Stern

Even though many semantic segmentation methods exist that are able to perform well on many medical datasets, often, they are not designed for direct use in clinical practice.

Organ Segmentation Segmentation +1

Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization

1 code implementation20 Sep 2021 Franz Thaler, Christian Payer, Martin Urschler, Darko Stern

In landmark localization, due to ambiguities in defining their exact position, landmark annotations may suffer from large observer variabilities, which result in uncertain annotations.

Decision Making

Variational Inference and Bayesian CNNs for Uncertainty Estimation in Multi-Factorial Bone Age Prediction

no code implementations25 Feb 2020 Stefan Eggenreich, Christian Payer, Martin Urschler, Darko Štern

Additionally to the extensive use in clinical medicine, biological age (BA) in legal medicine is used to assess unknown chronological age (CA) in applications where identification documents are not available.

Age Estimation Variational Inference

Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization

2 code implementations2 Aug 2019 Christian Payer, Darko Štern, Horst Bischof, Martin Urschler

In many medical image analysis applications, often only a limited amount of training data is available, which makes training of convolutional neural networks (CNNs) challenging.


Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks

no code implementations6 Jun 2018 Christian Payer, Darko Štern, Thomas Neff, Horst Bischof, Martin Urschler

Furthermore, we train the network with a novel embedding loss based on cosine similarities, such that the network predicts unique embeddings for every instance throughout videos.

Instance Segmentation Segmentation +1

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