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Dong Nie

Researcher at University of North Carolina at Chapel Hill

Publications -  67
Citations -  5709

Dong Nie is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 26, co-authored 67 publications receiving 3783 citations. Previous affiliations of Dong Nie include Alibaba Group & Shanghai Jiao Tong University.

Papers
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Posted ContentDOI

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Book ChapterDOI

Medical Image Synthesis with Context-Aware Generative Adversarial Networks

Dong Nie, +6 more
TL;DR: Wang et al. as mentioned in this paper trained a fully convolutional network (FCN) to generate CT given the MR image, and applied Auto-Context Model (ACM) to implement a context-aware generative adversarial network.
Journal ArticleDOI

Medical Image Synthesis with Deep Convolutional Adversarial Networks

Dong Nie, +7 more
- 09 Mar 2018 - 
TL;DR: This paper trains a fully convolutional network to generate a target image given a source image and proposes to use the adversarial learning strategy to better model the FCN, designed to incorporate an image-gradient-difference-based loss function to avoid generating blurry target images.
Proceedings ArticleDOI

Fully convolutional networks for multi-modality isointense infant brain image segmentation

Dong Nie, +3 more
TL;DR: This paper proposes to use fully convolutional networks (FCNs) for the segmentation of isointense phase brain MR images, and shows that the proposed model significantly outperformed previous methods in terms of accuracy and indicated a better way of integrating multi-modality images, which leads to performance improvement.
Book ChapterDOI

ASDNet: Attention Based Semi-supervised Deep Networks for Medical Image Segmentation

Dong Nie, +3 more
TL;DR: A fully convolutional confidence network is proposed to adversarially train the segmentation network and a region-attention based semi-supervised learning strategy to include the unlabeled data for training is proposed.