D
Dong Yang
Researcher at Nvidia
Publications - 117
Citations - 3241
Dong Yang is an academic researcher from Nvidia. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 20, co-authored 96 publications receiving 1443 citations. Previous affiliations of Dong Yang include Siemens & Princeton University.
Papers
More filters
Journal ArticleDOI
Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.
Stephanie Harmon,Thomas Sanford,Sheng Xu,Evrim B. Turkbey,Holger R. Roth,Ziyue Xu,Dong Yang,Andriy Myronenko,Victoria L. Anderson,Amel Amalou,Maxime Blain,Michael T. Kassin,Dilara Long,Nicole Varble,Nicole Varble,Stephanie M. Walker,Ulas Bagci,Anna Maria Ierardi,Elvira Stellato,Guido Giovanni Plensich,Giuseppe Franceschelli,Cristiano Girlando,Giovanni Irmici,Dominic Labella,Dima A. Hammoud,Ashkan A. Malayeri,Elizabeth C. Jones,Ronald M. Summers,Peter L. Choyke,Daguang Xu,Mona Flores,Kaku Tamura,Hirofumi Obinata,Hitoshi Mori,Francesca Patella,Maurizio Cariati,Gianpaolo Carrafiello,Gianpaolo Carrafiello,Peng An,Bradford J. Wood,Baris Turkbey +40 more
TL;DR: It is shown that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity.
Journal ArticleDOI
Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation
Ling Zhang,Xiaosong Wang,Dong Yang,Thomas Sanford,Stephanie Harmon,Baris Turkbey,Bradford J. Wood,Holger R. Roth,Andriy Myronenko,Daguang Xu,Ziyue Xu +10 more
TL;DR: A deep stacked transformation approach for domain generalization that can be generalized to the design of highly robust deep segmentation models for clinical deployment and reaches the performance of state-of theart fully supervised models that are trained and tested on their source domains.
PatentDOI
Automatic Liver Segmentation Using Adversarial Image-to-Image Network
TL;DR: In this paper, a method and apparatus for automated liver segmentation in a 3D medical image of a patient is disclosed, where the 3D computed tomography (CT) volume is input to a trained deep image-to-image network.
Book ChapterDOI
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
TL;DR: Wang et al. as mentioned in this paper proposed a novel segmentation model termed Swin UNEt TRansformers (Swin UNETR), which reformulated the task of 3D brain tumor semantic segmentation as a sequence to sequence prediction problem.
Journal ArticleDOI
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Yingda Xia,Dong Yang,Zhiding Yu,Fengze Liu,Jinzheng Cai,Lequan Yu,Zhuotun Zhu,Daguang Xu,Alan L. Yuille,Holger R. Roth +9 more
TL;DR: This paper proposes uncertainty-aware multi-view co-training (UMCT), a unified framework that addresses these two tasks for volumetric medical image segmentation and can even effectively handle the challenging situation where labeled source data is inaccessible.