Biography

I am pursuing my Ph.D. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests include computer vision, medical image and video analysis, and AIGC.

Research and Interests

• Computer Vision
• Medical Image and Video Analysis
• AIGC

Publications

• Weixuan Liu, Bairui Zhang, Tao Liu, Juntao Jiang, and Yong Liu. Artificial Intelligence in Pancreatic Image Analysis: A Review. Sensors, 24:4749, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel’s workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.

  @article{jiang2024aii,
  title = {Artificial Intelligence in Pancreatic Image Analysis: A Review},
  author = {Weixuan Liu and Bairui Zhang and Tao Liu and Juntao Jiang and Yong Liu},
  year = 2024,
  journal = {Sensors},
  volume = 24,
  pages = {4749},
  doi = {10.3390/s24144749},
  abstract = {Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel's workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.}
  }

• Juntao Jiang, Xiyu Chen, Guanzhong Tian, and Yong Liu. VIG-UNET: Vision Graph Neural Networks for Medical Image Segmentation. In IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Deep neural networks have been widely used in medical image analysis and medical image segmentation is one of the most important tasks. U-shaped neural networks with encoder-decoder are prevailing and have succeeded greatly in various segmentation tasks. While CNNs treat an image as a grid of pixels in Euclidean space and Transformers recognize an image as a sequence of patches, graph-based representation is more generalized and can construct connections for each part of an image. In this paper, we propose a novel ViG-UNet, a graph neural network-based U-shaped architecture with the encoder, the decoder, the bottleneck, and skip connections. The downsampling and upsampling modules are also carefully designed. The experimental results on ISIC 2016, ISIC 2017 and Kvasir-SEG datasets demonstrate that our proposed architecture outperforms most existing classic and state-of-the-art U-shaped networks.

  @inproceedings{jiang2023vig,
  title = {VIG-UNET: Vision Graph Neural Networks for Medical Image Segmentation},
  author = {Juntao Jiang and Xiyu Chen and Guanzhong Tian and Yong Liu},
  year = 2023,
  booktitle = {IEEE 20th International Symposium on Biomedical Imaging (ISBI)},
  doi = {10.1109/ISBI53787.2023.10230496},
  abstract = {Deep neural networks have been widely used in medical image analysis and medical image segmentation is one of the most important tasks. U-shaped neural networks with encoder-decoder are prevailing and have succeeded greatly in various segmentation tasks. While CNNs treat an image as a grid of pixels in Euclidean space and Transformers recognize an image as a sequence of patches, graph-based representation is more generalized and can construct connections for each part of an image. In this paper, we propose a novel ViG-UNet, a graph neural network-based U-shaped architecture with the encoder, the decoder, the bottleneck, and skip connections. The downsampling and upsampling modules are also carefully designed. The experimental results on ISIC 2016, ISIC 2017 and Kvasir-SEG datasets demonstrate that our proposed architecture outperforms most existing classic and state-of-the-art U-shaped networks.}
  }