.png){kind=icon}
Guang Yang
H指数:69
被引数:2.0万
个人简介
A researcher in the Bioengineering Department and Imperial-X at Imperial College London. Their work focuses on developing AI-based techniques for imaging and biomedical data analysis with translational potential. The research encompasses AI-driven data quality transfer, medical data analysis, and applications in pre-clinical and clinical studies. Current projects include data-driven fast imaging, data harmonization, data synthesis, federated learning, explainable AI, foundation models, and generative AI, with applications such as drug discovery. The research addresses clinical challenges in areas including ageing, cardiovascular disease, lung disease, and oncology. The scholar is also interested in collaborating with industrial partners on innovative projects.
教育背景
暂无内容
工作经历
2023.07 – 至今
Imperial College London, Bioengineering Department and Imperial-X, United Kingdom
Associate Professor
荣誉奖项
暂无内容
解耦多对比超分辨率：无监督配对跨模态合成的自监督隐式再表示
自监督跨传感器光谱图像融合：一种跨域多模态互学习框架
通过2.5-D径向分割和图傅里叶重建从超声心动图进行胎儿左心室4-D重建
Medical Image Analysis
IF11.8
基于游戏的学习在异步人工智能素养课程中的应用：提升学生对人工智能认知、行为、情感和伦理学习的方法
基于混合上下文动态核的全局到局部状态空间模型用于医学图像分类
染料一致性学习：处理染料变化以实现自动数字病理分割
解剖学指导的放射学报告生成与病理学感知区域提示
优化了交替多片活体心脏扩散张量成像的视场和脂肪抑制方法
循环自监督扩散用于超高场至高场MRI合成
从粗糙到连续：用于运动鲁棒各向异性MRI重建的渐进式细化隐式神经表示
SAFE-Det：尺度自适应特征增强用于遥感小目标检测
显式可微分切片与全局变形在心脏网格重建中的应用
Medical Image Analysis
IF11.8
基于几何深度学习的准确且泛化性强的蛋白质-配体结合亲和力预测
一种高效、可扩展且自适应的即插即用时序注意力模块，用于运动引导的心脏分割，并带有稀疏时序标签
Medical Image Analysis
IF11.8
SeLoRA: 自扩展LoRA用于高质量和高效医学图像合成
Pattern Recognition
IF7.6
外科手术场景下的视觉问答任务用Mamba2增强的多模态大型语言模型
通过可插入时空注意力模块实现运动增强的心脏解剖分割
基于多模态融合与不确定性建模的脑肿瘤分割可靠框架
Information Fusion
IF15.5
