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Xiaoqian Zhang — Medical Imaging & Machine Learning
Xiaoqian Zhang at Shanghai Jiao Tong University. Medical imaging, representation learning, computational pathology, and research software.
Posts
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portfolio
publications
Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology
Published in arXiv preprint arXiv:2408.15032, 2024
State-space-duality-based multiple instance learning for efficient long-sequence modeling in computational pathology and whole-slide image analysis.
Citation: Yuqi Zhang, Xiaoqian Zhang, Jiakai Wang, Yuancheng Yang, Taiying Peng, and Chao Tong. (2024). "Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology." arXiv preprint arXiv:2408.15032.
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多模态基础模型与超声影像的交叉应用
Published in 中华医学超声杂志（电子版）, 2025
A review of multimodal foundation models, their cross-applications with ultrasound imaging, and emerging opportunities for clinical medical AI.
Citation: 孟雅清, 杨景涵, 李欣玥, 张啸谦, 田捷, 王坤. (2025). "多模态基础模型与超声影像的交叉应用." 中华医学超声杂志（电子版）, 22(08), 777-782.
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Research Progress and Clinical Translation of Photoacoustic–Ultrasound Fusion Imaging in Breast Cancer Diagnosis and Therapy
Published in Advanced Ultrasound in Diagnosis and Therapy, 9(4), 467–482, 2025
A review of photoacoustic–ultrasound fusion imaging, AI-enhanced interpretation, and clinical translation for breast cancer diagnosis and therapy.
Citation: Xiaoqian Zhang, Jingwen Zhang, Yijie Dong, and Jianqiao Zhou. (2025). "Research Progress and Clinical Translation of Photoacoustic–Ultrasound Fusion Imaging in Breast Cancer Diagnosis and Therapy." Advanced Ultrasound in Diagnosis and Therapy, 9(4), 467–482.
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MoMIL: Multi-order Enhanced Multiple Instance Learning for Computational Pathology
Published in Image and Vision Computing, 167, 105918, 2026
A journal article on multi-order enhanced multiple instance learning for stronger whole-slide representation learning in computational pathology.
Citation: Yuqi Zhang, Xiaoqian Zhang, Jiakai Wang, Baoyu Liang, Yuancheng Yang, and Chao Tong. (2026). "MoMIL: Multi-order Enhanced Multiple Instance Learning for Computational Pathology." Image and Vision Computing, 167, 105918.
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VQPET: Leveraging Vector-Quantized Codebook Prior for PET Image Synthesis
Published in IEEE Transactions on Medical Imaging, 45(5), 2542–2560, 2026
A two-stage PET synthesis framework that learns a vector-quantized codebook prior from high-quality PET data and uses it to guide synthesis.
Citation: H. Chen, Z. Yang, Y. Zhou, X. Zhang, H. Zhang, D. Zhao, B. Wei, G. Zhou, and Y. Xu. (2026). "VQPET: Leveraging Vector-Quantized Codebook Prior for PET Image Synthesis." IEEE Transactions on Medical Imaging, 45(5), 2542–2560.
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