Wentao Zhu

Orcid: 0000-0002-7505-9512

Affiliations:
  • NVIDIA, Bethesda, USA
  • University of California, Department of Computer Science, Irvine, USA


According to our database1, Wentao Zhu authored at least 26 papers between 2016 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

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Bibliography

2023
Towards Comprehensive Monocular Depth Estimation: Multiple Heads are Better Than One.
IEEE Trans. Multim., 2023

UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation.
CoRR, 2023

AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge.
Medical Image Anal., 2022

MONAI: An open-source framework for deep learning in healthcare.
CoRR, 2022

Bandwidth-Aware Adaptive Codec for DNN Inference Offloading in IoT.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Multi-Domain Image Completion for Random Missing Input Data.
IEEE Trans. Medical Imaging, 2021

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan.
Medical Image Anal., 2021

Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

Test-Time Training for Deformable Multi-Scale Image Registration.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020
NeurReg: Neural Registration and Its Application to Image Segmentation.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2020

LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer.
Proceedings of the 28th International Conference on Computational Linguistics, 2020

2019
Deep Learning for Automated Medical Image Analysis.
PhD thesis, 2019

Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking.
CoRR, 2019

Deep Learning for Automated Medical Image Analysis.
CoRR, 2019

Cardiac Segmentation of LGE MRI with Noisy Labels.
Proceedings of the Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges, 2019

Privacy-Preserving Federated Brain Tumour Segmentation.
Proceedings of the Machine Learning in Medical Imaging - 10th International Workshop, 2019

2018
AnatomyNet: Deep 3D Squeeze-and-excitation U-Nets for fast and fully automated whole-volume anatomical segmentation.
CoRR, 2018

DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification.
Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision, 2018

DeepEM: Deep 3D ConvNets with EM for Weakly Supervised Pulmonary Nodule Detection.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

Adversarial deep structured nets for mass segmentation from mammograms.
Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018

2017
DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification.
CoRR, 2017

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017

2016
Adversarial Deep Structural Networks for Mammographic Mass Segmentation.
CoRR, 2016

Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016


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