257 citations · 383 across the 15 of their papers we have counts for
15 papers
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…
AutoPtosis
Abdullah Aleem, Manoj Prabhakar Nallabothula, Pete Setabutr +2
Blepharoptosis, or ptosis as it is more commonly referred to, is a condition of the eyelid where the upper eyelid droops. The current diagnosis for ptosis involves cumbersome manua…
I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications
Nooshin Mojab, Vahid Noroozi, Abdullah Aleem +8
Data from clinical real-world settings is characterized by variability in quality, machine-type, setting, and source. One of the primary goals of medical computer vision is to deve…
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…
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…
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…
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…
CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss
Blaine Rister, Darvin Yi, Kaushik Shivakumar +2
Fully-convolutional neural networks have achieved superior performance in a variety of image segmentation tasks. However, their training requires laborious manual annotation of lar…
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…
Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors
Darvin Yi, Rebecca Lynn Sawyer, David Cohn +4
Breast cancer has the highest incidence and second highest mortality rate for women in the US. Our study aims to utilize deep learning for benign/malignant classification of mammog…
The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI
Zhao Chen, Darvin Yi
We present a vision-only model for gaming AI which uses a late integration deep convolutional network architecture trained in a purely supervised imitation learning context. Althou…
3-D Convolutional Neural Networks for Glioblastoma Segmentation
Darvin Yi, Mu Zhou, Zhao Chen +1
Convolutional Neural Networks (CNN) have emerged as powerful tools for learning discriminative image features. In this paper, we propose a framework of 3-D fully CNN models for Gli…