257 citations · 266 across the 8 of their papers we have counts for
8 papers
A Green Solution for Breast Region Segmentation Using Deep Active Learning
Sam Narimani, Solveig Roth Hoff, Kathinka Dæhli Kurz +3
Purpose: Annotation of medical breast images is an essential step toward better diagnostic but a time consuming task. This research aims to focus on different selecting sample stra…
Sustainable Deep Learning-Based Breast Lesion Segmentation: Impact of Breast Region Segmentation on Performance
Sam Narimani, Solveig Roth Hoff, Kathinka Dahli Kurz +3
Purpose: Segmentation of the breast lesion in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an essential step to accurately diagnose and plan treatment and moni…
Comparative Analysis of Deep Learning Architectures for Breast Region Segmentation with a Novel Breast Boundary Proposal
Sam Narimani, Solveig Roth Hoff, Kathinka Dæhli Kurz +3
Purpose: Segmentation of the breast region in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for the automatic measurement of breast density and the qu…
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…