Vision foundation model for 3D magnetic resonance imaging segmentation, classification, and registration [0.03%]
面向3D磁共振图像分割、分类和配准的视觉基础模型
Shansong Wang,Mojtaba Safari,Qiang Li et al.
Shansong Wang et al.
Vision foundation models (VFMs) are pre-trained on extensive image datasets to learn general representations. These models can subsequently be fine-tuned for specific downstream tasks, markedly boosting performance across a broad range of a...
A hypergraph-based model for tumor prognosis using local and global information fusion on H&E-stained histology images [0.03%]
基于超图的肿瘤预后模型，使用H&E染色病理图像的局部和全局信息融合
Chao Tang,Jun Liu,Yanfen Cui et al.
Chao Tang et al.
Prognostic variables play a critical role in guiding clinical treatment decisions for cancer patients. However, extracting prognostic information from gigapixel histopathology slides remains a significant challenge. While attention-based de...
Quality-label-free fetal brain MRI quality control based on image orientation recognition uncertainty [0.03%]
基于图像方向识别不确定性的无质量标签的胎儿大脑MRI质量控制
Mingxuan Liu,Yi Liao,Haoxiang Li et al.
Mingxuan Liu et al.
Quality control (QC) in fetal MRI is essential for efficient, high-quality data acquisition and analysis aimed at assessing fetal brain development and detecting abnormalities. Supervised deep learning methods require numerous image-quality...
Channel-wise joint disentanglement representation learning for B-mode and super-resolution ultrasound based CAD of breast cancer [0.03%]
基于B模式和超分辨率乳腺超声的乳腺癌智能检测的通道级联合解缠表示学习方法
Yuhang Zheng,Jiale Xu,Qing Hua et al.
Yuhang Zheng et al.
B-mode ultrasound (BUS) is widely used in breast cancer diagnosis, while the emerging super-resolution ultrasound (SRUS) provides microvascular information with high spatial resolution, which has shown great potential in improving breast ca...
An efficient, scalable, and adaptable plug-and-play temporal attention module for motion-guided cardiac segmentation with sparse temporal labels [0.03%]
一种高效、可扩展且适应性强的即插即用时间注意力模块，用于稀疏时间标签引导的心脏运动分割
Md Kamrul Hasan,Guang Yang,Choon Hwai Yap
Md Kamrul Hasan
Cardiac anatomy segmentation is essential for clinical assessment of cardiac function and disease diagnosis to inform treatment and intervention. Deep learning (DL) has improved cardiac anatomy segmentation accuracy, especially when informa...
Artifact-suppressed 3D retinal microvascular segmentation via multi-scale topology regulation [0.03%]
基于多尺度拓扑调节的抑制人工痕迹的三维视网膜微血管分割方法
Ting Luo,Jinxian Zhang,Tao Chen et al.
Ting Luo et al.
Optical coherence tomography angiography (OCTA) enables non-invasive visualization of retinal microvasculature, and accurate 3D vessel segmentation is essential for quantifying biomarkers critical for early diagnosis and monitoring of diabe...
Diversity-driven MG-MAE: Multi-granularity representation learning for non-salient object segmentation [0.03%]
多样性驱动的MG-MAE：非显著物体分割的多粒度表征学习
Chengjin Yu,Bin Zhang,Chenchu Xu et al.
Chengjin Yu et al.
Masked Autoencoders (MAEs) have grown increasingly prominent as a powerful self-supervised learning paradigm. They are capable of effectively leveraging inherent image prior information and are gaining traction in the field of medical image...
Physics-informed graph neural networks for flow field estimation in carotid arteries [0.03%]
基于物理信息的图神经网络在颈动脉流场估计中的应用
Julian Suk,Dieuwertje Alblas,Barbara A Hutten et al.
Julian Suk et al.
Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology such as atherosclerosis. Non-invasive, in-vivo measurement of these quantities can only be performed using a select number of modalities that are not w...
Guiqiu Liao,Matjaž Jogan,Marcel Hussing et al.
Guiqiu Liao et al.
Object-centric slot attention is a powerful framework for unsupervised learning of structured and explainable representations that can support reasoning about objects and actions, including in surgical video. However, current object-centric...
Baizhi Wang,Kun Zhang,Yuhao Wang et al.
Baizhi Wang et al.
Each gigapixel whole slide image (WSI) contains tens of thousands of patches, many of which are redundant, leading to significant computational, storage, and transmission overhead. This motivates the need for automatic WSI summarization, wh...