2 citations · 2 across the 5 of their papers we have counts for
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Decoupling Disease, Covariates, and Individual Variability: A Unified Disentanglement Framework for Medical Image Classification
Shengjie Zhang, Jinglin Zhang, Zhuangzhuang Jiang +10
Accurately isolating disease-related features from confounding covariates (e.g., age, gender, site) and individual variations remains a fundamental challenge in medical image class…
UMind-VL: A Generalist Ultrasound Vision-Language Model for Unified Grounded Perception and Comprehensive Interpretation
Dengbo Chen, Ziwei Zhao, Kexin Zhang +9
Despite significant strides in medical foundation models, the ultrasound domain lacks a comprehensive solution capable of bridging low-level Ultrasound Grounded Perception (e.g., s…
Autoregressive Sequence Modeling for 3D Medical Image Representation
Siwen Wang, Churan Wang, Fei Gao +4
Three-dimensional (3D) medical images, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), are essential for clinical applications. However, the need for diverse…
Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation
Fei Gao, Siwen Wang, Fandong Zhang +5
Medical image analysis suffers from a shortage of data, whether annotated or not. This becomes even more pronounced when it comes to 3D medical images. Self-Supervised Learning (SS…