Xiaojiao Xiao

Orcid: 0000-0003-2444-4004

According to our database1, Xiaojiao Xiao authored at least 10 papers between 2016 and 2024.

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

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
FgC2F-UDiff: Frequency-Guided and Coarse-to-Fine Unified Diffusion Model for Multi-Modality Missing MRI Synthesis.
IEEE Trans. Computational Imaging, 2024

Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning and Deep Learning Techniques.
Proceedings of the 2024 9th International Conference on Mathematics and Artificial Intelligence, 2024

2023
Edge-Aware Multi-task Network for Integrating Quantification Segmentation and Uncertainty Prediction of Liver Tumor on Multi-modality Non-contrast MRI.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

2022
Task relevance driven adversarial learning for simultaneous detection, size grading, and quantification of hepatocellular carcinoma via integrating multi-modality MRI.
Medical Image Anal., 2022

2021
United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI.
Medical Image Anal., 2021

Segmentation of Liver Lesions Without Contrast Agents With Radiomics-Guided Densely UNet-Nested GAN.
IEEE Access, 2021

mfTrans-Net: Quantitative Measurement of Hepatocellular Carcinoma via Multi-Function Transformer Regression Network.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

2019
A feature extraction method for lung nodules based on a multichannel principal component analysis network (PCANet).
Multim. Tools Appl., 2019

Radiomics-guided GAN for Segmentation of Liver Tumor Without Contrast Agents.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

2016
A Deep Learning Model of Automatic Detection of Pulmonary Nodules Based on Convolution Neural Networks (CNNs).
Proceedings of the Bio-inspired Computing - Theories and Applications, 2016


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