World's Best Scientists 2026 revealed!

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Computer Science

D-Index
36
Citations
7882
World Ranking
11052
National Ranking
4593

Overview

Dong Nie is a researcher affiliated with the University of North Carolina at Chapel Hill in the United States. Their work spans several intersecting fields, including computer science, medicine, and engineering. The primary areas of study focus on computer vision and pattern recognition, radiology, nuclear medicine and imaging, artificial intelligence, biomedical engineering, and neurology.

The research topics covered by Dong Nie include:

  • Radiomics and machine learning in medical imaging
  • Advanced neural network applications
  • Medical image segmentation techniques
  • Medical imaging and analysis
  • Medical imaging techniques and applications
  • AI in cancer detection
  • Brain tumor detection and classification

Dong Nie has published papers in various scientific venues, commonly in journals and conferences related to medical imaging and artificial intelligence. Frequent publication venues include:

  • UNC Libraries
  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • Medical Image Analysis
  • Knowledge-Based Systems

Several coauthors have frequently collaborated with Dong Nie, including:

  • Dinggang Shen
  • Qian Wang
  • Jun Lian
  • Zhenyu Tang
  • Xuyun Wen

Examples of recent research papers authored or coauthored by Dong Nie cover topics such as medical image segmentation, crowd counting, and radiotherapy dose prediction. Selected recent publications include:

  • "Unified medical image segmentation by learning from uncertainty in an end-to-end manner" (2022), published in Knowledge-Based Systems
  • "Hybrid Graph Neural Networks for Crowd Counting" (2020), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • "HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation in CT Images" (2021), published in IEEE Transactions on Medical Imaging
  • "Adversarial Confidence Learning for Medical Image Segmentation and Synthesis" (2020), published in International Journal of Computer Vision
  • "Explainable attention guided adversarial deep network for 3D radiotherapy dose distribution prediction" (2022), published in Knowledge-Based Systems

Best Publications

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

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • Medical Image Synthesis with Context-Aware Generative Adversarial Networks

    Dong Nie;Roger Trullo;Jun Lian;Caroline Petitjean

  • Medical Image Synthesis with Deep Convolutional Adversarial Networks

    Dong Nie;Roger Trullo;Jun Lian;Li Wang

  • 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients.

    Dong Nie;Han Zhang;Ehsan Adeli;Luyan Liu

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

    Dong Nie;Li Wang;Yaozong Gao;Dinggang Sken

  • Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI

    Lei Xiang;Yu Qiao;Dong Nie;Le An

  • Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks.

    Dong Nie;Xiaohuan Cao;Yaozong Gao;Li Wang

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

    Dong Nie;Yaozong Gao;Li Wang;Dinggang Shen

  • Deformable Image Registration based on Similarity-Steered CNN Regression.

    Xiaohuan Cao;Jianhua Yang;Jun Zhang;Dong Nie

  • High-Resolution Encoder–Decoder Networks for Low-Contrast Medical Image Segmentation

    Sihang Zhou;Dong Nie;Ehsan Adeli;Jianping Yin

  • Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages

    Dong Nie;Junfeng Lu;Han Zhang;Ehsan Adeli

  • Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge

    Li Wang;Dong Nie;Guannan Li;Elodie Puybareau

  • Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image.

    Lei Xiang;Qian Wang;Dong Nie;Lichi Zhang

  • 3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation

    Dong Nie;Li Wang;Ehsan Adeli;Cuijin Lao

  • Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images

    Xuhua Ren;Lei Xiang;Dong Nie;Yeqin Shao

  • Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis

    Mingxia Liu;Jun Zhang;Dong Nie;Pew-Thian Yap

  • Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks

    Kelei He;Xiaohuan Cao;Yinghuan Shi;Dong Nie

  • Segmentation of Organs at Risk in thoracic CT images using a SharpMask architecture and Conditional Random Fields

    R. Trullo;C. Petitjean;S. Ruan;B. Dubray

  • CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.

    Shuai Wang;Kelei He;Dong Nie;Sihang Zhou

  • HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation in CT Images

    Kelei He;Chunfeng Lian;Bing Zhang;Xin Zhang

  • Medical Image Synthesis with Context-Aware Generative Adversarial Networks

    Dong Nie;Roger Trullo;Caroline Petitjean;Su Ruan

Frequent Co-Authors

Dinggang Shen
Dinggang Shen ShanghaiTech University
Qian Wang
Qian Wang Shanghai Jiao Tong University
Yaozong Gao
Yaozong Gao United Imaging Healthcare (China)
Tingshao Zhu
Tingshao Zhu University of Chinese Academy of Sciences
Su Ruan
Su Ruan University of Rouen
Ehsan Adeli
Ehsan Adeli Stanford University
Han Zhang
Han Zhang ShanghaiTech University
Yu Qiao
Yu Qiao Chinese Academy of Sciences
Gang Li
Gang Li University of North Carolina at Chapel Hill
Feng Shi
Feng Shi United Imaging Intelligence (China)

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