activity
20162023
most citedExploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT

172 citations · 394 across the 11 of their papers we have counts for

collaborators
Showing 2021Show all

5 papers · 1 filter

eess.IV2021

4D iterative reconstruction of brain fMRI in the moving fetus

Athena Taymourtash, Hamza Kebiri, Sébastien Tourbier +8

Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful imaging technique for studying functional development of the brain in utero. However, unpredictable and exc…

eess.IV2021

Pseudo-domains in imaging data improve prediction of future disease status in multi-center studies

Matthias Perkonigg, Peter Mesenbrink, Alexander Goehler +3

In multi-center randomized clinical trials imaging data can be diverse due to acquisition technology or scanning protocols. Models predicting future outcome of patients are impaire…

cs.CV2021

Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics

Matthias Perkonigg, Johannes Hofmanninger, Christian Herold +2

Machine learning in medical imaging during clinical routine is impaired by changes in scanner protocols, hardware, or policies resulting in a heterogeneous set of acquisition setti…

cs.CV2021

Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI

Lucas Fidon, Michael Aertsen, Nada Mufti +13

The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and ro…

cs.LG2021

Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition

Matthias Perkonigg, Johannes Hofmanninger, Georg Langs

Imaging in clinical routine is subject to changing scanner protocols, hardware, or policies in a typically heterogeneous set of acquisition hardware. Accuracy and reliability of de…