Search Results for author: Darvin Yi

Found 15 papers, 0 papers with code

CvS: Classification via Segmentation For Small Datasets

no code implementations29 Oct 2021 Nooshin Mojab, Philip S. Yu, Joelle A. Hallak, Darvin Yi

The success of deep learning methods relies heavily on the availability of a large amount of data.



no code implementations7 Jun 2021 Abdullah Aleem, Manoj Prabhakar Nallabothula, Pete Setabutr, Joelle A. Hallak, Darvin Yi

Blepharoptosis, or ptosis as it is more commonly referred to, is a condition of the eyelid where the upper eyelid droops.

I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications

no code implementations30 Mar 2021 Nooshin Mojab, Vahid Noroozi, Abdullah Aleem, Manoj P. Nallabothula, Joseph Baker, Dimitri T. Azar, Mark Rosenblatt, RV Paul Chan, Darvin Yi, Philip S. Yu, Joelle A. Hallak

In this paper, we present a new multi-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic Database Atlas (I-ODA), with the goal of advancing state-of-the-art computer vision applications in ophthalmology, and improving upon the translatable capacity of AI based applications across different clinical settings.

Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization

no code implementations23 Feb 2020 Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel Rubin

We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation.


Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives

no code implementations MIDL 2019 Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel Rubin

Even with a simulated false negative rate as high as 50%, applying our loss function to randomly censored data preserves maximum sensitivity at 97% of the baseline with uncensored training data, compared to just 10% for a standard loss function.

Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study

no code implementations27 Dec 2019 Endre Grøvik, Darvin Yi, Michael Iv, Elizabeth Tong, Line Brennhaug Nilsen, Anna Latysheva, Cathrine Saxhaug, Kari Dolven Jacobsen, Åslaug Helland, Kyrre Eeg Emblem, Daniel Rubin, Greg Zaharchuk

A deep learning based segmentation model for automatic segmentation of brain metastases, named DropOut, was trained on multi-sequence MRI from 100 patients, and validated/tested on 10/55 patients.


MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation

no code implementations18 Dec 2019 Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Kyrre Eeg Emblem, Line Brennhaug Nilsen, Cathrine Saxhaug, Anna Latysheva, Kari Dolven Jacobsen, Åslaug Helland, Greg Zaharchuk, Daniel Rubin

We illustrate not only the generalizability of the network but also the utility of this robustness when applying the trained model to data from a different center, which does not use the same pulse sequences.

Small Data Image Classification

DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences

no code implementations25 Apr 2019 Ameya Phalak, Zhao Chen, Darvin Yi, Khushi Gupta, Vijay Badrinarayanan, Andrew Rabinovich

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.

Clustering Depth Estimation

Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI

no code implementations18 Mar 2019 Endre Grøvik, Darvin Yi, Michael Iv, Elisabeth Tong, Daniel L. Rubin, Greg Zaharchuk

For an optimal probability threshold, detection and segmentation performance was assessed on a per metastasis basis.

CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss

no code implementations27 Nov 2018 Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, Daniel L. Rubin

To achieve the best results from data augmentation, our model uses the intersection-over-union (IOU) loss function, a close relative of the Dice loss.

Data Augmentation Image Segmentation +4

The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI

no code implementations18 Feb 2017 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.

Imitation Learning Q-Learning

3-D Convolutional Neural Networks for Glioblastoma Segmentation

no code implementations14 Nov 2016 Darvin Yi, Mu Zhou, Zhao Chen, Olivier Gevaert

In this paper, we propose a framework of 3-D fully CNN models for Glioblastoma segmentation from multi-modality MRI data.


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