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07/14/2023
cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations
Classification of heterogeneous diseases is challenging due to their com...
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05/30/2023
atTRACTive: Semi-automatic white matter tract segmentation using active learning
Accurately identifying white matter tracts in medical images is essentia...
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05/26/2023
Shape-based pose estimation for automatic standard views of the knee
Surgical treatment of complicated knee fractures is guided by real-time ...
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04/09/2023
Transformer Utilization in Medical Image Segmentation Networks
Owing to success in the data-rich domain of natural images, Transformers...
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03/17/2023
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
There has been exploding interest in embracing Transformer-based archite...
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05/12/2021
The Federated Tumor Segmentation (FeTS) Challenge
This manuscript describes the first challenge on Federated Learning, nam...
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04/12/2021
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is increasing at an eno...
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12/15/2020
Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks
Image registration is the basis for many applications in the fields of m...
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11/15/2020
Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI
Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnos...
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11/27/2019
High- and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection
Variational Auto-Encoders have often been used for unsupervised pretrain...
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07/04/2019
Unsupervised Anomaly Localization using Variational Auto-Encoders
An assumption-free automatic check of medical images for potentially ove...
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06/06/2018
Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions
International challenges have become the standard for validation of biom...
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11/28/2017
Adversarial Networks for Prostate Cancer Detection
The large number of trainable parameters of deep neural networks renders...
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11/27/2017
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning
Purpose: Due to the breakthrough successes of deep learning-based soluti...
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02/26/2017