390 citations · 826 across the 15 of their papers we have counts for
15 papers
Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI
Marijn Borghouts, Richard McKinley, Manuel Köstner +3
Distinguishing acute ischemic strokes (AIS) from stroke mimics (SMs), particularly in cases involving medium and small vessel occlusions, remains a significant diagnostic challenge…
CortexMorph: fast cortical thickness estimation via diffeomorphic registration using VoxelMorph
Richard McKinley, Christian Rummel
The thickness of the cortical band is linked to various neurological and psychiatric conditions, and is often estimated through surface-based methods such as Freesurfer in MRI stud…
Leveraging SO(3)-steerable convolutions for pose-robust semantic segmentation in 3D medical data
Ivan Diaz, Mario Geiger, Richard Iain McKinley
Convolutional neural networks (CNNs) allow for parameter sharing and translational equivariance by using convolutional kernels in their linear layers. By restricting these kernels…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
Florian Kofler, Ivan Ezhov, Fabian Isensee +16
Metrics optimized in complex machine learning tasks are often selected in an ad-hoc manner. It is unknown how they align with human expert perception. We explore the correlations b…
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty
Richard McKinley, Micheal Rebsamen, Katrin Daetwyler +3
The BraTS dataset contains a mixture of high-grade and low-grade gliomas, which have a rather different appearance: previous studies have shown that performance can be improved by…
ModelHub.AI: Dissemination Platform for Deep Learning Models
Ahmed Hosny, Michael Schwier, Christoph Berger +13
Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among…
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation
Raphael Meier, Michael Rebsamen, Urspeter Knecht +3
Deep learning methods for brain tumor segmentation are typically trained in an ad hoc fashion on all available data. Brain tumors are tremendously heterogeneous in image appearance…
Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence
Richard McKinley, Lorenz Grunder, Rik Wepfer +12
The detection of new or enlarged white-matter lesions in multiple sclerosis is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclero…
Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks
Richard McKinley, Michael Rebsamen, Raphael Meier +3
In applications of supervised learning applied to medical image segmentation, the need for large amounts of labeled data typically goes unquestioned. In particular, in the case of…
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser +41
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are o…
Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
Richard McKinley, Rik Wepfer, Fabian Aschwanden +10
Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper w…
Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning
Andreas Hess, Raphael Meier, Johannes Kaesmacher +5
In this work, we present a novel convolutional neural net- work based method for perfusion map generation in dynamic suscepti- bility contrast-enhanced perfusion imaging. The propo…