390 citations · 667 across the 12 of their papers we have counts for
12 papers
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.…
Combining unsupervised and supervised learning for predicting the final stroke lesion
Adriano Pinto, Sérgio Pereira, Raphael Meier +4
Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-…
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…
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…
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…