3-D Convolutional Neural Networks for Glioblastoma Segmentation. - Darvin Yi; Mu Zhou; Zhao Chen; Olivier Gevaert (CoRR, 2016)

Differential Data Augmentation Techniques for Medical Imaging Classification Tasks. - Zeshan Hussain; Francisco Gimenez; Darvin Yi; Daniel L. Rubin (AMIA, 2017)

The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI. - Zhao Chen; Darvin Yi (CoRR, 2017)

Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors. - Darvin Yi; Rebecca Lynn Sawyer; David Cohn III; Jared Dunnmon; Carson K. Lam; Xuerong Xiao; Daniel L. Rubin (CoRR, 2017)

Institutionally Distributed Deep Learning Networks. - Ken Chang; Niranjan Balachandar; Carson K. Lam; Darvin Yi; James M. Brown 001; Andrew Beers; Bruce R. Rosen; Daniel L. Rubin; Jayashree Kalpathy-Cramer (CoRR, 2017)

CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss. - Blaine Rister; Darvin Yi; Kaushik Shivakumar; Tomomi Nobashi; Daniel L. Rubin (CoRR, 2018)

Distributed deep learning networks among institutions for medical imaging. - Ken Chang; Niranjan Balachandar; Carson K. Lam; Darvin Yi; James M. Brown 001; Andrew Beers; Bruce R. Rosen; Daniel L. Rubin; Jayashree Kalpathy-Cramer (J. Am. Medical Informatics Assoc., 2018)

Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI. - Endre Grøvik; Darvin Yi; Michael Iv; Elisabeth Tong; Daniel L. Rubin; Greg Zaharchuk (CoRR, 2019)

DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences. - Ameya Phalak; Zhao Chen 006; Darvin Yi; Khushi Gupta; Vijay Badrinarayanan; Andrew Rabinovich (CoRR, 2019)

MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation. - Darvin Yi; Endre Grøvik; Michael Iv; Elisabeth Tong; Kyrre Eeg Emblem; Line Brennhaug Nilsen; Cathrine Saxhaug; Anna Latysheva; Kari Dolven Jacobsen; Åslaug Helland; Greg Zaharchuk; Daniel L. Rubin (CoRR, 2019)

Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study. - Endre Grøvik; Darvin Yi; Michael Iv; Elizabeth Tong; Line Brennhaug Nilsen; Anna Latysheva; Cathrine Saxhaug; Kari Dolven Jacobsen; Åslaug Helland; Kyrre Eeg Emblem; Daniel L. Rubin; Greg Zaharchuk (CoRR, 2019)

Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings. - Nooshin Mojab; Vahid Noroozi; Darvin Yi; Manoj Prabhakar Nallabothula; Abdullah Aleem; Philip S. Yu; Joelle A. Hallak (ICMLA, 2020)

Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives. - Darvin Yi; Endre Grøvik; Michael Iv; Elizabeth Tong; Greg Zaharchuk; Daniel L. Rubin (MIDL, 2020)

Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization. - Darvin Yi; Endre Grøvik; Michael Iv; Elizabeth Tong; Greg Zaharchuk; Daniel L. Rubin (CoRR, 2020)

I-ODA, Real-world Multi-modal Longitudinal Data for Ophthalmic Applications. - Nooshin Mojab; Vahid Noroozi; Abdullah Aleem; Manoj Prabhakar Nallabothula; Joseph Baker; Dimitri T. Azar; Mark Rosenblatt; R. V. Paul Chan; Darvin Yi; Philip S. Yu; Joelle A. Hallak (HEALTHINF, 2021)

CvS: Classification via Segmentation For Small Datasets. - Nooshin Mojab; Philip S. Yu; Joelle A. Hallak; Darvin Yi (BMVC, 2021)

I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications. - Nooshin Mojab; Vahid Noroozi; Abdullah Aleem; Manoj Prabhakar Nallabothula; Joseph Baker; Dimitri T. Azar; Mark Rosenblatt; R. V. Paul Chan; Darvin Yi; Philip S. Yu; Joelle A. Hallak (CoRR, 2021)

AutoPtosis. - Abdullah Aleem; Manoj Prabhakar Nallabothula; Pete Setabutr; Joelle A. Hallak; Darvin Yi (CoRR, 2021)

Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study. - Endre Grøvik; Darvin Yi; Michael Iv; Elizabeth Tong; Line Brennhaug Nilsen; Anna Latysheva; Cathrine Saxhaug; Kari Dolven Jacobsen; Åslaug Helland; Kyrre Eeg Emblem; Daniel L. Rubin; Greg Zaharchuk (npj Digit. Medicine, 2021)

2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data. - Jon André Ottesen; Darvin Yi; Elizabeth Tong; Michael Iv; Anna Latysheva; Cathrine Saxhaug; Kari Dolven Jacobsen; Åslaug Helland; Kyrre Eeg Emblem; Daniel L. Rubin; Atle Bjørnerud; Greg Zaharchuk; et al. (Frontiers Neuroinformatics, 2022)

Repurposing the Image Generative Potential: Exploiting GANs to Grade Diabetic Retinopathy. - Isabella Poles; Eleonora D'Arnese; Luca G. Cellamare; Marco D. Santambrogio; Darvin Yi (CVPR Workshops, 2024)

A Physiological Variability Inspired Spatial Regularization for Joint Rigid-Deformable Abdominal MR Image Registration. - Isabella Poles; Eleonora D'Arnese; Darvin Yi; Marco D. Santambrogio (ISBI, 2024)

State-of-the-Art Periorbital Distance Prediction and Disease Classification Using Periorbital Features. - George R. Nahass; Ghasem Yazdanpanah; Madison Cheung; Alex Palacios; Jeffery Peterson; Kevin Heinze; Sasha Hubschman; Chad A. Purnell; Pete Setabutr; Ann Q. Tran; Darvin Yi (CoRR, 2024)

Open-Source Periorbital Segmentation Dataset for Ophthalmic Applications. - George R. Nahass; Emma Koehler; Nicholas Tomaras; Danny Lopez; Madison Cheung; Alexander Palacios; Jeffery Peterson; Sasha Hubschman; Kelsey Green; Chad A. Purnell; Pete Setabutr; Ann Q. Tran; et al. (CoRR, 2024)

Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review. - Mahtab Faraji; Homa Rashidisabet; George R. Nahass; RV Paul Chan; Thasarat S. Vajaranant; Darvin Yi (CoRR, 2024)

Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images. - George R. Nahass; Zhu Wang; Homa Rashidisabet; Won Hwa Kim; Sasha Hubschman; Jeffrey C. Peterson; Ghasem Yazdanpanah; Chad A. Purnell; Pete Setabutr; Ann Q. Tran; Darvin Yi; Sathya N. Ravi (CoRR, 2025)