49 citations · 81 across the 11 of their papers we have counts for
8 papers · 1 filter
CvS: Classification via Segmentation For Small Datasets
Nooshin Mojab, Philip S. Yu, Joelle A. Hallak +1
Deep learning models have shown promising results in a wide range of computer vision applications across various domains. The success of deep learning methods relies heavily on the…
Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings
Nooshin Mojab, Vahid Noroozi, Darvin Yi +4
With promising results of machine learning based models in computer vision, applications on medical imaging data have been increasing exponentially. However, generalizations to com…
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…
DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences
Ameya Phalak, Zhao Chen, Darvin Yi +3
We present DeepPerimeter, a deep learning based pipeline for inferring a full indoor perimeter (i.e. exterior boundary map) from a sequence of posed RGB images. Our method relies o…
Institutionally Distributed Deep Learning Networks
Ken Chang, Niranjan Balachandar, Carson K Lam +6
Deep learning has become a promising approach for automated medical diagnoses. When medical data samples are limited, collaboration among multiple institutions is necessary to achi…