DSFNet: Dual-source and spatiotemporal-feature fusion network for bedside diagnosis of lung injuries with electrical impedance tomography [0.03%]
基于电气阻抗断层成像的床旁诊断肺损伤的双源时空特征融合网络（DSFNet）
Zhiwei Li,Yang Wu,Kai Liu et al.
Zhiwei Li et al.
Electrical Impedance Tomography (EIT) is a promising tool for non-invasive and real-time lung monitoring, but the data heterogeneity and low spatial resolution limit its ability to diagnose lung injuries. To address these challenges, we pro...
Context-enriched contrastive auto-encoder with topology learning for medical hyperspectral image classification to diagnose tumors [0.03%]
上下文增强的对比自编码器与拓扑学习在医学高光谱图像分类中的应用以诊断肿瘤
Meiling Wang,Changda Xing,Yifang Wu et al.
Meiling Wang et al.
Deep learning has emerged as a highly effective approach for the automatic classification of medical hyperspectral images (MedHSIs), facilitating the accurate diagnosis of diverse tumors. Most of current methods suffer from the challenges i...
Jiwei Shan,Zeyu Cai,Cheng-Tai Hsieh et al.
Jiwei Shan et al.
Reconstructing deformable surgical scenes from endoscopic videos is a challenging task with important clinical applications. Recent state-of-the-art approaches, such as those based on implicit neural representations or 3D Gaussian splatting...
SurgLaVi: Large-scale hierarchical dataset for surgical vision-language representation learning [0.03%]
SurgLaVi：用于手术视觉语言表示学习的大规模分层数据集
Alejandra Perez,Chinedu Nwoye,Ramtin Raji Kermani et al.
Alejandra Perez et al.
Vision-language pre-training (VLP) offers unique advantages for surgery by aligning language with surgical videos, enabling workflow understanding and transfer across tasks without relying on expert-labeled datasets. However, progress in su...
SAM-driven cross prompting with adaptive sampling consistency for semi-supervised medical image segmentation [0.03%]
自适应采样一致性的SAM驱动的跨提示方法用于半监督医学图像分割
Juzheng Miao,Cheng Chen,Yuchen Yuan et al.
Juzheng Miao et al.
Semi-supervised learning (SSL) has achieved notable progress in medical image segmentation. To achieve effective SSL, a model needs to be able to efficiently learn from limited labeled data and effectively exploit knowledge from abundant un...
Cellflow: Advancing pathological image augmentation from spatial views to temporal trajectories [0.03%]
细胞流动：从空间视图到时间轨迹的病理图像增强技术
Zeyu Liu,Tianyi Zhang,Yufang He et al.
Zeyu Liu et al.
Deep learning has advanced pathological image analysis but remains constrained by limited annotated data, especially for fine-grained diagnostic tasks such as tumor subtyping, grading, and cellularity assessment. While data augmentation all...
Neurobridge: Bridging functional and structural brain networks via neural coupling and consistency-Guided dynamic graph learning [0.03%]
神经桥：通过神经耦合和一致性引导的动态图学习连接功能脑网络和结构脑网络
Yueying Li,Rui Dong,Xiaoyun Liu et al.
Yueying Li et al.
Modern medical imaging provides important insights into brain network analysis. Functional brain networks are used to characterize the functional connectivity patterns in resting or task states, and structural brain networks reflect the int...
Generative data-engine foundation model for universal few-shot 2D vascular image segmentation [0.03%]
通用少样本2D血管图像分割的生成数据引擎基础模型
Rongjun Ge,Xin Li,Yuxing Liu et al.
Rongjun Ge et al.
The segmentation of 2D vascular structures via deep learning holds significant clinical value but is hindered by the scarcity of annotated data, severely limiting its widespread application. Developing a universal few-shot vascular segmenta...
Asymmetric fiber orientation distribution estimation via unsupervised deep learning [0.03%]
基于无监督深度学习的非对称纤维取向分布估计方法研究
Di Zhang,Ziyu Li,Xiaofeng Deng et al.
Di Zhang et al.
Diffusion magnetic resonance imaging (dMRI) tractography is a key technique for reconstructing brain structural connectivity. A widely recognized limitation in tractography is the enforced symmetry of fiber orientation distribution function...
Deformation-Recovery diffusion model (DRDM): Instance deformation for image manipulation and synthesis [0.03%]
变形恢复扩散模型（DRDM）：图像操纵和合成的实例变形技术
Jian-Qing Zheng,Yuanhan Mo,Yang Sun et al.
Jian-Qing Zheng et al.
In medical imaging, diffusion models have shown great potential for synthetic image generation. However, these approaches often lack interpretable correspondence between generated and real images and can create anatomically implausible stru...