5 citations · 13 across the 6 of their papers we have counts for
6 papers
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 for Prostate Pathology
Okyaz Eminaga, Yuri Tolkach, Christian Kunder +13
The current study detects different morphologies related to prostate pathology using deep learning models; these models were evaluated on 2,121 hematoxylin and eosin (H&E) stain hi…
A Deep-learning Approach for Prognosis of Age-Related Macular Degeneration Disease using SD-OCT Imaging Biomarkers
Imon Banerjee, Luis de Sisternes, Joelle Hallak +4
We propose a hybrid sequential deep learning model to predict the risk of AMD progression in non-exudative AMD eyes at multiple timepoints, starting from short-term progression (3-…
Computerized Multiparametric MR image Analysis for Prostate Cancer Aggressiveness-Assessment
Imon Banerjee, Lewis Hahn, Geoffrey Sonn +2
We propose an automated method for detecting aggressive prostate cancer(CaP) (Gleason score >=7) based on a comprehensive analysis of the lesion and the surrounding normal prostate…