Xiangyuan Ma
Orcid: 0000-0003-0958-1537
According to our database1,
Xiangyuan Ma
authored at least 13 papers
between 2018 and 2023.
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Bibliography
2023
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge.
CoRR, 2023
Multi-view based computer-aided model with anatomical position prior for architectural distortion detection in digital breast tomosynthesis.
Proceedings of the Medical Imaging 2023: Computer-Aided Diagnosis, 2023
2022
W-TextCNN: A TextCNN model with weighted word embeddings for Chinese address pattern classification.
Comput. Environ. Urban Syst., 2022
Urban Multiple Route Planning Model Using Dynamic Programming in Reinforcement Learning.
IEEE Trans. Intell. Transp. Syst., 2022
Computer-aided detection for architectural distortion: a comparison of digital breast tomosynthesis and digital mammography.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022
2021
A points of interest matching method using a multivariate weighting function with gradient descent optimization.
Trans. GIS, 2021
Architectural distortion detection in digital breast tomosynthesis with adaptive receptive field and adaptive convolution kernel shape.
Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021
2020
Assessing multiscale visual appearance characteristics of neighbourhoods using geographically weighted principal component analysis in Shenzhen, China.
Comput. Environ. Urban Syst., 2020
Architectural distortion detection approach guided by mammary gland spatial pattern in digital breast tomosynthesis.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
2019
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
2018
The Application of the Analytic Hierarchy Process and a New Correlation Algorithm to Urban Construction and Supervision Using Multi-Source Government Data in Tianjin.
ISPRS Int. J. Geo Inf., 2018
Fully automated pectoral muscle identification on MLO-view mammograms with deep convolutional neural network.
Proceedings of the 14th International Workshop on Breast Imaging, 2018