Tiange Xiang
Orcid: 0000-0003-3311-1287
According to our database1,
Tiange Xiang
authored at least 20 papers
between 2019 and 2024.
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Bibliography
2024
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images.
IEEE Trans. Pattern Anal. Mach. Intell., September, 2024
Intelligent Caching for Seamless High-Quality Streaming in Vehicular Networks: A Multi-Agent Reinforcement Learning Approach.
IEEE Trans. Intell. Veh., February, 2024
ICT Express, 2024
Physical-space Multi-body Mesh Detection Achieved by Local Alignment and Global Dense Learning.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024
2023
DDM<sup>2</sup>: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023
2022
DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis.
IEEE Trans. Medical Imaging, 2022
Medical Image Anal., 2022
2021
3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis.
CoRR, 2021
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021
2020
BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020
2019
Integration of Multimodal Data for Breast Cancer Classification Using a Hybrid Deep Learning Method.
Proceedings of the Intelligent Computing Theories and Application, 2019