Tracking spatial temporal details in ultrasound long video via wavelet analysis and memory bank [0.03%]
基于小波分析和记忆库的超声长视频时空细节追踪方法
Chenxiao Zhang,Runshi Zhang,Junchen Wang
Chenxiao Zhang
Medical ultrasound videos are widely used for medical inspections, disease diagnosis and surgical planning. High-fidelity lesion area and target organ segmentation constitutes a key component of the computer-assisted surgery workflow. The l...
Diagnostic text-guided representation learning in hierarchical classification for pathological whole slide image [0.03%]
基于诊断文本的病理全滑动图像分层分类表示学习方法
Jiawen Li,Qiehe Sun,Renao Yan et al.
Jiawen Li et al.
With the development of digital imaging in medical microscopy, artificial intelligent-based analysis of pathological whole slide images (WSIs) provides a powerful tool for cancer diagnosis. Limited by the expensive cost of pixel-level annot...
AGFS-tractometry: A novel atlas-guided fine-scale tractometry approach for enhanced along-tract group statistical comparison using diffusion MRI tractography [0.03%]
基于图谱的精细尺度纤维束迹踪计量法（AGFS-tractometry）：一种使用扩散加权磁共振成像纤维束追踪技术进行沿纤维束的组间统计比较的新方法
Ruixi Zheng,Wei Zhang,Yijie Li et al.
Ruixi Zheng et al.
Diffusion MRI (dMRI) tractography is currently the only method for in vivo mapping of the brain's white matter (WM) connections. Tractometry is an advanced tractography analysis technique for along-tract profiling to investigate the morphol...
Jonas M Van Elburg,Natalia V Korobova,Mohammad M Islam et al.
Jonas M Van Elburg et al.
Dynamic contrast-enhanced (DCE) MRI is a powerful technique for detecting and characterising various diseases by quantifying tissue perfusion. However, accurate perfusion quantification remains challenging due to noisy data and the complexi...
PISCO: Self-supervised k-space regularization for improved neural implicit k-space representations of dynamic MRI [0.03%]
基于自监督k空间正则化的改进动态MRI神经隐式k空间表示方法
Veronika Spieker,Hannah Eichhorn,Wenqi Huang et al.
Veronika Spieker et al.
Neural implicit k-space representations (NIK) have shown promising results for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet, reducing acquisition time, and thereby available training data, results in severe per...
Perivascular space identification nnUNet for generalised usage (PINGU) [0.03%]
用于通用性的血管周围空间识别nnUNet(PINGU)
Benjamin Sinclair,William Pham,Lucy Vivash et al.
Benjamin Sinclair et al.
Perivascular spaces (PVSs) form a central component of the brain's waste clearance system, the glymphatic system. These structures are visible on MRIs when enlarged, and their morphology is associated with aging and neurological disease. Ma...
D-EDL: Differential evidential deep learning for robust medical out-of-distribution detection [0.03%]
差异证据深度学习在鲁棒医学异常检测中的应用
Wei Fu,Yufei Chen,Yuqi Liu et al.
Wei Fu et al.
In computer-aided diagnosis, the extreme imbalance in disease incidence rates often results in the omission of rare conditions, leading to out-of-distribution (OOD) samples during testing. To prevent unreliable diagnostic outputs, detecting...
Spatial transcriptomics expression prediction from histopathology based on cross-modal mask reconstruction and contrastive learning [0.03%]
基于跨模态掩码重建和对比学习的病理组织学空间转录组表达预测
Junzhuo Liu,Markus Eckstein,Zhixiang Wang et al.
Junzhuo Liu et al.
Spatial transcriptomics is a technology that captures gene expression at different spatial locations, widely used in tumor microenvironment analysis and molecular profiling of histopathology, providing valuable insights into resolving gene ...
Robust simultaneous multislice MRI reconstruction using slice-wise learned generative diffusion priors [0.03%]
基于片层学习生成扩散先验的鲁棒多重切片MRI重建方法
Shoujin Huang,Guanxiong Luo,Yunlin Zhao et al.
Shoujin Huang et al.
Simultaneous multislice (SMS) imaging is a powerful technique for accelerating magnetic resonance imaging (MRI) acquisitions. However, SMS reconstruction remains challenging due to complex signal interactions between and within the excited ...
Ark+: Supervised training a single high-performance AI foundation model from many differently labeled datasets-no label consolidation required [0.03%]
ARK+: 在无需标签整合的情况下，利用多个不同标注的数据集监督训练单一高性能AI基础模型
DongAo Ma,Jiaxuan Pang,Shivasakthi Senthil Velan et al.
DongAo Ma et al.
This article presents a methodological breakthrough in supervised learning for training a single, robust, and high-performance artificial intelligence (AI) model using a multitude of datasets labeled differently-yet requiring no manual labe...