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04/17/2023
Predicting dynamic, motion-related changes in B0 field in the brain at a 7 T MRI using a subject-specific fine-tuned U-net
Subject movement during the magnetic resonance examination is inevitable...
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02/11/2022
Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging Data
Motion correction is an essential preprocessing step in functional Magne...
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11/25/2021
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics
Machine learning in medical imaging during clinical routine is impaired ...
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11/22/2021
4D iterative reconstruction of brain fMRI in the moving fetus
Resting-state functional Magnetic Resonance Imaging (fMRI) is a powerful...
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11/15/2021
Pseudo-domains in imaging data improve prediction of future disease status in multi-center studies
In multi-center randomized clinical trials imaging data can be diverse d...
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08/09/2021
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
The performance of deep neural networks typically increases with the num...
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06/07/2021
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition
Imaging in clinical routine is subject to changing scanner protocols, ha...
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08/28/2020
The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification
Malignant melanoma (MM) is one of the deadliest types of skin cancer. An...
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07/06/2020
Dynamic memory to alleviate catastrophic forgetting in continuous learning settings
In medical imaging, technical progress or changes in diagnostic procedur...
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02/03/2020
Separation of target anatomical structure and occlusions in chest radiographs
Chest radiographs are commonly performed low-cost exams for screening an...
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01/31/2020
Automatic lung segmentation in routine imaging is a data diversity problem, not a methodology problem
Automated segmentation of anatomical structures is a crucial step in man...
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01/31/2020
Unsupervised deep clustering for predictive texture pattern discovery in medical images
Predictive marker patterns in imaging data are a means to quantify disea...
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05/29/2019
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
Diagnosis and treatment guidance are aided by detecting relevant biomark...
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01/24/2019
Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Optical coherence tomography (OCT) has become the most important imaging...
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10/31/2018
Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
The identification and quantification of markers in medical images is cr...
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06/22/2018
Keypoint Transfer for Fast Whole-Body Segmentation
We introduce an approach for image segmentation based on sparse correspo...
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05/08/2018
Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images
The automatic detection of disease related entities in retinal imaging d...
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03/17/2017
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Obtaining models that capture imaging markers relevant for disease progr...
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01/06/2017
NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)
This workshop explores the interface between cognitive neuroscience and ...
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12/02/2016