A Deep Spatio-Temporal Architecture for Dynamic ECN Analysis with Granger Causality based Causal Discovery [0.03%]
基于Granger因果关系的因果发现的动态ECN分析Deep时空架构
Faming Xu,Yiding Wang,Gang Qu et al.
Faming Xu et al.
Neurobrain science provides the motivation for research on causal modeling. The existing causal discovery methods have shown promising results in effective connectivity network analysis, however, they often overlook the dynamics of causalit...
Medical image segmentation using dual-decoder mutual teaching with a mean teacher framework [0.03%]
基于平均教师的双解码协同教学的医学图像分割方法
Juan Zhang,Gaoqiang Jiang,Zhongwen Li et al.
Juan Zhang et al.
Accurate segmentation of medical images is essential for many clinical applications and is now typically achieved by training deep learning models on large annotated datasets. However, acquiring sufficient labeled images remains challenging...
Multi-graph Graph matching for coronary artery semantic labeling in invasive coronary angiograms [0.03%]
基于多图匹配的冠状动脉语义标注方法研究
Chen Zhao,Zhihui Xu,Pukar Baral et al.
Chen Zhao et al.
Coronary artery disease (CAD) stands as the leading cause of death worldwide, and invasive coronary angiography (ICA) remains the gold standard for assessing vascular anatomical information. However, deep learning-based methods encounter ch...
A graph transformer-based foundation model for brain functional connectivity network [0.03%]
基于图形变换器的脑功能连接网络基础模型
Yulong Wang,Vince D Calhoun,Godfrey D Pearlson et al.
Yulong Wang et al.
Although foundation models have advanced many medical imaging fields, their absence in neuroimage analysis limits progress in neuroscience and clinical practice. Brain functional connectivity (FC) analysis is central to understanding brain ...
Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI [0.03%]
基于结构磁共振图像的脑解剖先验建模预测认知障碍的临床进展
Lintao Zhang,Jinjian Wu,Lihong Wang et al.
Lintao Zhang et al.
Brain structural MRI has been widely used to assess the future progression of cognitive impairment (CI). Previous learning-based studies usually suffer from the issue of small-sized labeled training data, while a huge amount of structural M...
Yuang Wang,Siyeop Yoon,Pengfei Jin et al.
Yuang Wang et al.
Diffusion-based models have demonstrated remarkable effectiveness in image restoration tasks; however, their iterative denoising process, which starts from Gaussian noise, often leads to slow inference speeds. The Image-to-Image Schrödinge...
Local Sliced Wasserstein Feature Sets for Illumination Invariant Face Recognition [0.03%]
用于光照不变的人脸识别的局部切片Wasserstein特征集
Yan Zhuang,Shiying Li,Mohammad Shifat-E-Rabbi et al.
Yan Zhuang et al.
We present a new method for face recognition from digital images acquired under varying illumination conditions. The method is based on mathematical modeling of local gradient distributions using the Radon Cumulative Distribution Transform ...
MS-TCRNet: Multi-Stage Temporal Convolutional Recurrent Networks for Action Segmentation Using Sensor-Augmented Kinematics [0.03%]
多阶段时序卷积循环网络：利用传感器增强动力学的动作分割方法
Adam Goldbraikh,Omer Shubi,Or Rubin et al.
Adam Goldbraikh et al.
Action segmentation is a challenging task in high-level process analysis, typically performed on video or kinematic data obtained from various sensors. This work presents two contributions related to action segmentation on kinematic data. F...
Source-free collaborative domain adaptation via multi-perspective feature enrichment for functional MRI analysis [0.03%]
基于多视角特征增强的无源协作领域适应功能磁共振成像分析方法
Yuqi Fang,Jinjian Wu,Qianqian Wang et al.
Yuqi Fang et al.
Resting-state functional MRI (rs-fMRI) is increasingly employed in multi-site research to analyze neurological disorders, but there exists cross-site/domain data heterogeneity caused by site effects such as differences in scanners/protocols...
Assessment of Volumetric Dense Tissue Segmentation in Tomosynthesis Using Deep Virtual Clinical Trials [0.03%]
使用深度虚拟临床试验评估tomosynthesis中的体积致密组织分割
B Barufaldi,J V Gomes,Tm Silva Filho et al.
B Barufaldi et al.
The adoption of artificial intelligence (AI) in medical imaging requires careful evaluation of machine-learning algorithms. We propose the use of a "deep virtual clinical trial" (DeepVCT) method to effectively evaluate the performance of AI...