257 citations · 266 across the 5 of their papers we have counts for
5 papers
Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization
Darvin Yi, Endre Grøvik, Michael Iv +3
We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation. We create our ensemble by training each network on our data…
Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives
Darvin Yi, Endre Grøvik, Michael Iv +3
Deep learning has proven to be an essential tool for medical image analysis. However, the need for accurately labeled input data, often requiring time- and labor-intensive annotati…
Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study
Endre Grøvik, Darvin Yi, Michael Iv +9
The purpose was to assess the clinical value of a novel DropOut model for detecting and segmenting brain metastases, in which a neural network is trained on four distinct MRI seque…
MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation
Darvin Yi, Endre Grøvik, Michael Iv +9
Magnetic resonance (MR) imaging is an essential diagnostic tool in clinical medicine. Recently, a variety of deep learning methods have been applied to segmentation tasks in medica…
Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI
Endre Grøvik, Darvin Yi, Michael Iv +3
Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multi-sequence 3D imaging. This study dem…