138 citations · 370 across the 19 of their papers we have counts for
6 papers · 1 filter
Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects
Ben Glocker, Robert Robinson, Daniel C. Castro +2
This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data. We utilize structural T1-weighted brain MRI obt…
PHiSeg: Capturing Uncertainty in Medical Image Segmentation
Christian F. Baumgartner, Kerem C. Tezcan, Krishna Chaitanya +6
Segmentation of anatomical structures and pathologies is inherently ambiguous. For instance, structure borders may not be clearly visible or different experts may have different st…
Medical Imaging with Deep Learning: MIDL 2019 -- Extended Abstract Track
M. Jorge Cardoso, Aasa Feragen, Ben Glocker +4
This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 Jul…
A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
Robin Brügger, Christian F. Baumgartner, Ender Konukoglu
One of the key drawbacks of 3D convolutional neural networks for segmentation is their memory footprint, which necessitates compromises in the network architecture in order to fit…
Semi-Supervised and Task-Driven Data Augmentation
Krishna Chaitanya, Neerav Karani, Christian Baumgartner +3
Supervised deep learning methods for segmentation require large amounts of labelled training data, without which they are prone to overfitting, not generalizing well to unseen imag…
Adversarial Augmentation for Enhancing Classification of Mammography Images
Lukas Jendele, Ondrej Skopek, Anton S. Becker +1
Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging. On t…