Mingyuan Meng/蒙明远

Mingyuan Meng/蒙明远

He/His

PhD candidate at the University of Sydney && Visiting scholar at Shanghai Jiao Tong University

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Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

Published:

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Blog Post number 1

less than 1 minute read

Published:

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portfolio

publications

DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT

Published in IEEE Journal of Biomedical and Health Informatics, 2022

Recommended citation: Mingyuan Meng, Bingxin Gu, Lei Bi, Shaoli Song, David Dagan Feng, and Jinman Kim. DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT. IEEE Journal of Biomedical and Health Informatics, 26(9):4497-4507, 2022.
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Multi-task Deep Learning-based Radiomic Nomogram for Prognostic Prediction in Locoregionally Advanced Nasopharyngeal Carcinoma

Published in European Journal of Nuclear Medicine and Molecular Imaging , 2023

Recommended citation: Bingxin Gu*, Mingyuan Meng*, Mingzhen Xu*, David Dagan Feng, Lei Bi, Jinman Kim, and Shaoli Song. Multi-task Deep Learning-based Radiomic Nomogram for Prognostic Prediction in Locoregionally Advanced Nasopharyngeal Carcinoma. European Journal of Nuclear Medicine and Molecular Imaging, 50:3996-4009, 2023.
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Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer

Published in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023

Recommended citation: Mingyuan Meng, Lei Bi, Michael Fulham, Dagan Feng, and Jinman Kim. Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 400-410, 2023.
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Non-iterative Coarse-to-Fine Transformer Networks for Joint Affine and Deformable Image Registration

Published in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023

Recommended citation: Mingyuan Meng, Lei Bi, Michael Fulham, Dagan Feng, and Jinman Kim. Non-iterative Coarse-to-Fine Transformer Networks for Joint Affine and Deformable Image Registration. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 750-760, 2023.
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3DPX: Progressive 2D-to-3D Oral Image Reconstruction with Hybrid MLP-CNN Networks

Published in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024

Recommended citation: Xiaoshuang Li, Mingyuan Meng, Lei Bi, Eduardo Delamare, Zimo Huang, Dagan Feng, Bin Sheng, and Jinman Kim. 3DPX: Progressive 2D-to-3D Oral Image Reconstruction with Hybrid MLP-CNN Networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024.

Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance

Published in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024

Recommended citation: Shuchang Ye, Mingyuan Meng, Mingjian Li, Dagan Feng, and Jinman Kim. Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2024.

Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration

Published in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

Presented as oral presentation and selected as best paper award candidate (Top 24) at CVPR 2024

Recommended citation: Mingyuan Meng, Dagan Feng, Lei Bi, and Jinman Kim. Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9645-9654, 2024.
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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