Jianghao Wu

I am a PhD student at Monash University, with a Master’s degree from the University of Electronic Science and Technology of China (UESTC), where I was supervised by Prof. Guotai Wang and Prof. Shaoting Zhang. I am a member of HiLab.

I focus on post-training adaptation of medical AI models, ranging from lightweight deep learning networks to large-scale foundation models. My research explores domain adaptation, test-time training, and self-supervised learning to enhance the reasoning-time robustness and generalizability of visual, language, and multi-modal systems in real-world medical scenarios.

news

Jun 18, 2025	Three papers are accepted by MICCAI 2025!
Jun 07, 2025	One paper is accepted by Neurocomputing!
Jan 03, 2025	One paper is accepted by ISBI 2025 as an oral presentation!
Oct 28, 2024	We achieved first place in the ACM MM 2024 Multi-rater Medical Image Segmentation (MMIS 2024) challenges for Radiotherapy Planning in Nasopharyngeal Carcinoma and Glioblastoma!
Oct 06, 2024	We won second place in the MICCAI 2024 MBH-Seg Challenge: Multi-class Brain Hemorrhage Segmentation in Non-contrast CT!

selected publications

ISBI

FPL-UDA: Filtered pseudo label-based unsupervised cross-modality adaptation for vestibular schwannoma segmentation

Jianghao Wu, Ran Gu, Guiming Dong, Guotai Wang, and Shaoting Zhang

In International Symposium on Biomedical Imaging (ISBI), 2022

Bib Code

@inproceedings{wu2022fpl,
  title = {FPL-UDA: Filtered pseudo label-based unsupervised cross-modality adaptation for vestibular schwannoma segmentation},
  author = {Wu, Jianghao and Gu, Ran and Dong, Guiming and Wang, Guotai and Zhang, Shaoting},
  booktitle = {International Symposium on Biomedical Imaging (ISBI)},
  pages = {1--5},
  year = {2022},
  price = {Oral},
}

IPMI

UPL-TTA: Uncertainty-aware pseudo label guided fully test time adaptation for fetal brain segmentation

Jianghao Wu, Ran Gu, Tao Lu, Shaoting Zhang, and Guotai Wang

In International Conference on Information Processing in Medical Imaging(IPMI), 2023

arXiv Bib

@inproceedings{wu2023upl,
  title = {UPL-TTA: Uncertainty-aware pseudo label guided fully test time adaptation for fetal brain segmentation},
  author = {Wu, Jianghao and Gu, Ran and Lu, Tao and Zhang, Shaoting and Wang, Guotai},
  booktitle = {International Conference on Information Processing in Medical Imaging(IPMI)},
  pages = {237--249},
  year = {2023},
}

ISBI

RPL-SFDA: Reliable Pseudo Label-Guided Source-Free Cross-Modality Adaptation for NPC GTV Segmentation

Xinya Liu, Jianghao Wu, Xiangde Luo, Wenjun Liao, Shichuan Zhang, Shaoting Zhang, and Guotai Wang

In International Symposium on Biomedical Imaging (ISBI), 2024

Bib

@inproceedings{liu2024rpl,
  title = {RPL-SFDA: Reliable Pseudo Label-Guided Source-Free Cross-Modality Adaptation for NPC GTV Segmentation},
  author = {Liu, Xinya and Wu, Jianghao and Luo, Xiangde and Liao, Wenjun and Zhang, Shichuan and Zhang, Shaoting and Wang, Guotai},
  booktitle = {International Symposium on Biomedical Imaging (ISBI)},
  pages = {1--5},
  year = {2024},
}

TMI

FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation

Jianghao Wu, Dong Guo, Guotai Wang, Qiang Yue, Huijun Yu, Kang Li, and Shaoting Zhang

IEEE Transactions on Medical Imaging (TMI), 2024

arXiv Bib Code

@article{wu2024fpl+,
  title = {FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation},
  author = {Wu, Jianghao and Guo, Dong and Wang, Guotai and Yue, Qiang and Yu, Huijun and Li, Kang and Zhang, Shaoting},
  journal = {IEEE Transactions on Medical Imaging (TMI)},
  year = {2024},
  volume = {43},
  number = {9},
  pages = {3098-3109},
}

TMI

UPL-SFDA: Uncertainty-aware pseudo label guided source-free domain adaptation for medical image segmentation

Jianghao Wu, Guotai Wang, Ran Gu, Tao Lu, Yinan Chen, Wentao Zhu, Tom Vercauteren, Sébastien Ourselin, and Shaoting Zhang

IEEE Transactions on Medical Imaging (TMI), 2023

arXiv Bib Code

@article{wu2023upm,
  title = {UPL-SFDA: Uncertainty-aware pseudo label guided source-free domain adaptation for medical image segmentation},
  author = {Wu, Jianghao and Wang, Guotai and Gu, Ran and Lu, Tao and Chen, Yinan and Zhu, Wentao and Vercauteren, Tom and Ourselin, S{\'e}bastien and Zhang, Shaoting},
  journal = {IEEE Transactions on Medical Imaging (TMI)},
  year = {2023},
  volume = {42},
  number = {12},
  pages = {3932-3943},
}

Neurocomputing

TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency

Jianghao Wu, Dong Guo, Lu Wang, Shuojue Yang, Yuanjie Zheng, Jonathan Shapey, Tom Vercauteren, Sotirios Bisdas, Robert Bradford, Shakeel Saeed, and others

Neurocomputing, 2023

Bib

@article{wu2023tiss,
  title = {TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency},
  author = {Wu, Jianghao and Guo, Dong and Wang, Lu and Yang, Shuojue and Zheng, Yuanjie and Shapey, Jonathan and Vercauteren, Tom and Bisdas, Sotirios and Bradford, Robert and Saeed, Shakeel and others},
  journal = {Neurocomputing},
  volume = {544},
  pages = {126295},
  year = {2023},
}

MedIA

A novel one-to-multiple unsupervised domain adaptation framework for abdominal organ segmentation

Xiaowei Xu, Yinan Chen, Jianghao Wu, Jiangshan Lu, Yuxiang Ye, Yechong Huang, Xin Dou, Kang Li, Guotai Wang, Shaoting Zhang, and others

Medical Image Analysis (MedIA), 2023

Bib

@article{xu2023novel,
  title = {A novel one-to-multiple unsupervised domain adaptation framework for abdominal organ segmentation},
  author = {Xu, Xiaowei and Chen, Yinan and Wu, Jianghao and Lu, Jiangshan and Ye, Yuxiang and Huang, Yechong and Dou, Xin and Li, Kang and Wang, Guotai and Zhang, Shaoting and others},
  journal = {Medical Image Analysis (MedIA)},
  volume = {88},
  pages = {102873},
  year = {2023},
}

arXiv

MIS-FM: 3d medical image segmentation using foundation models pretrained on a large-scale unannotated dataset

Guotai Wang, Jianghao Wu, Xiangde Luo, Xinglong Liu, Kang Li, and Shaoting Zhang

arXiv preprint arXiv:2306.16925, 2023

arXiv Bib Code

@article{wang2023mis,
  title = {MIS-FM: 3d medical image segmentation using foundation models pretrained on a large-scale unannotated dataset},
  author = {Wang, Guotai and Wu, Jianghao and Luo, Xiangde and Liu, Xinglong and Li, Kang and Zhang, Shaoting},
  journal = {arXiv preprint arXiv:2306.16925},
  year = {2023},
}