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Research Associate If you’re interested in medical image analysis or foundational models, I’d be glad to share insights, provide guidance, or explore research opportunities together. Don’t hesitate to get in touch. |
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Dr. Yuting He is currently a Research Associate in the Department of Biomedical Engineering at Case Western Reserve University, USA. He received his Ph.D. in Computer Science from Southeast University in 2023. His research lies at the forefront of medical representation learning, interactive medical AI, and foundation models for healthcare, with a focus on developing structure-aware and human-centered computational frameworks for multimodal medical data. Dr. He has published over 40 peer-reviewed articles in top-tier journals and conferences, including Nature Communications, IEEE T-PAMI, IEEE T-MI, Medical Image Analysis, CVPR, ICCV, MICCAI, etc. His scholarly contributions are widely recognized, evidenced by prestigious honors such as the Bao Gang Outstanding Student Award. In addition to his research, Dr. He is actively involved in the academic community, serving as an Area Chair for MICCAI 2026, Senior Program Committee member for IJCAI-ECAI 2026, and a regular reviewer for leading venues including IEEE T-MI, IEE T-NNLS, NeurIPS, CVPR, AAAI, etc.
My research focuses on the question: How can we design general medical AI systems that 1) learn unified representations across modalities and institutions, 2) incorporate human intent and interaction into perception, and decision-making, and 3) adapt reliably and efficiently to new clinical environments?
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Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglement. |
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Imaging foundation model for universal enhancement of non-ideal measurement CT. |
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Dynamic Stream Network for Combinatorial Explosion Problem in Deformable Medical Image Registration. |
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Generative Data-engine Foundation Model for Universal Few-shot 2D Vascular Image Segmentation. |
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Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision. |
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Adaptation follow human attention: Gaze-assisted medical segment anything model. |
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Human gaze-based dual teacher guidance learning for semi-supervised medical image segmentation. |
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Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning. |
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Conditional Virtual Imaging for Few-Shot Vascular Image Segmentation. |
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Foundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions. |
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Segment Anything in Medical Images. |
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Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training. |
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Multi-Task Learning for Pulmonary Arterial Hypertension Prognosis Prediction via Memory Drift and Prior Prompt Learning on 3D Chest CT. |
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Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness. |
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Few-shot Learning for Deformable Medical Image Registration with Perception-Correspondence Decoupling and Reverse Teaching. |
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Thin Semantics Enhancement via High-Frequency Priori Rule for Thin Structures Segmentation. |
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Meta grayscale adaptive network for 3D integrated renal structures segmentation. |
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EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation. |
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CPNet: cycle prototype network for weakly-supervised 3D renal compartments segmentation on CT images. |
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Deep complementary joint model for complex scene registration and few-shot segmentation on medical images. |
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Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation. |
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