Learning dual-scale context with overlap awareness for keypoint-driven partial-overlap medical image registration [0.03%]
一种学习双尺度重叠感知上下文的基于关键点的医学图像配准方法
Jia Mi,Caiwen Jiang,Xiaosong Xiong et al.
Jia Mi et al.
Aligning medical images with partial anatomical overlap presents a significant challenge for various clinical applications that involve comparison of images with varying Fields of View. However, most existing registration methods implicitly...
From structural complexity to causal representation: A dynamic fractal-attention framework for fine-grained ovarian tumor classification in ultrasound [0.03%]
从结构复杂性到因果表征：一种动态分形注意力框架在超声卵巢肿瘤细粒度分类中的应用
Qingyuan Zhang,Qiuyue Fu,Xuping Zhang et al.
Qingyuan Zhang et al.
Accurate fine-grained classification of ovarian tumors from ultrasound images remains challenging due to speckle noise, boundary ambiguity, structural heterogeneity, and acquisition-induced spurious correlations. To address these issues, we...
BundleParc: Consistent white matter bundle parcellation without tractography [0.03%]
BundleParc：无需轨迹图的一致性白质束分割
Antoine Théberge,Zineb El Yamani,Muhamed Barakovic et al.
Antoine Théberge et al.
Tractometry, also known as tract profiling, is a powerful technique for probing microstructural properties along white matter (WM) tracts. A prerequisite for tractography-based tractometry is bundle parcellation-the subdivision of WM bundle...
3D vessel reconstruction from sparse-view dynamic DSA images via vessel probability guided attenuation learning [0.03%]
基于血管概率的衰减学习 sparse-view 动态DSA 血管图像三维重建方法
Zhentao Liu,Huangxuan Zhao,Wenhui Qin et al.
Zhentao Liu et al.
Digital Subtraction Angiography (DSA) is one of the gold standards for vascular disease diagnosis. With the help of a contrast agent, time-resolved 2D DSA images deliver comprehensive blood flow information and can be utilized to reconstruc...
FedSemiDG: Domain generalized federated semi-supervised medical image segmentation [0.03%]
FedSemiDG：领域泛化的联邦半监督医学图像分割
Zhipeng Deng,Zhe Xu,Tsuyoshi Isshiki et al.
Zhipeng Deng et al.
Medical image segmentation is challenging due to the diversity of medical images and the lack of labeled data, which motivates recent developments in federated semi-supervised learning (FSSL) to leverage a large amount of unlabeled data fro...
ISDR-Net: Interpretable Self-Supervised Differentiable Rendering Network for monocular dynamic sensor-head pose tracking and registration [0.03%]
基于可解释自监督可微渲染的单目动态传感头姿态跟踪与配准网络(ISDR-Net)
Xingwen Fu,Yuqing Yang,Ruonan Wang et al.
Xingwen Fu et al.
Magnetoencephalography (MEG) offers high temporal and spatial resolution for clinical and neuroscience applications. Traditional sensor registration methods depend on complex point cloud reconstruction, which is error-prone, labor-intensive...
X2Shape: CT-free 3D multi-organ reconstruction with biplanar X-rays [0.03%]
基于双平面X射线的CT-Free 3D多器官重建(X2Shape)
Zhaohong Pan,Haowei Zhou,Qi Ren et al.
Zhaohong Pan et al.
Reconstructing three-dimensional (3D) anatomy from routine X-ray imaging remains a long-standing challenge, promising high accessibility and minimal radiation exposure compared to computed tomography (CT). We propose X2Shape, a deep learnin...
ZScribbleSeg: A comprehensive segmentation framework with modeling of efficient annotation and maximization of scribble supervision [0.03%]
ZScribbleSeg：一种全面的分割框架，包含高效的注释建模和最大化草图监督的方法
Ke Zhang,Bomin Wang,Hangqi Zhou et al.
Ke Zhang et al.
Curating fully annotated datasets for medical image segmentation is labor-intensive and expertise-demanding. To alleviate this problem, prior studies have explored scribble annotations for weakly supervised segmentation. Existing solutions ...
SPACT: A clustering-driven multi-modal framework for survival prediction using genomic and histopathology data [0.03%]
基于基因组和组织病理学数据的生存预测的聚类驱动多模态框架SPACT
Fatma Ezgi Öğülmüş,Shahaddin Gafarov,Yasin Almalıoğlu et al.
Fatma Ezgi Öğülmüş et al.
Multi-modal data-based algorithms have gained attention in their capabilities in prediction tasks in cancer-related research. This paper introduces SPACT, a multi-modal capable of predicting cancer survival probability based on a deep-learn...
Zifeng Lian,Jiameng Liu,Jiawei Huang et al.
Zifeng Lian et al.
Accurate and efficient reconstruction of cortical surfaces from MRI throughout the lifespan is essential for mapping normal and abnormal brain development, maturation, and aging, and for facilitating early diagnosis of neurodevelopmental an...