Unsupervised adaptive sampling graph autoencoder for 3D surface encoding and mesh representation transfer [0.03%]
一种用于三维表面编码和网格表示转换的无监督自适应采样图自动编码器
Ines A Cruz-Guerrero,Joseph Nagel,Antonio R Porras
Ines A Cruz-Guerrero
Standardizing anatomical surface representations across heterogeneous datasets is critical for population-level modeling and downstream medical image analysis tasks. Although existing mesh autoencoders can achieve standardized latent surfac...
Welcome new doctor: Continual learning with expert consultation and autoregressive inference for whole slide image analysis [0.03%]
欢迎新医生：基于专家咨询和自回归推理的全片扫描图像分析中的终身学习方法
Doanh C Bui,Jin Tae Kwak
Doanh C Bui
Whole Slide Image (WSI) analysis, with its ability to reveal detailed tissue structures in magnified views, plays a crucial role in cancer diagnosis and prognosis. Due to their giga-sized nature, WSIs require substantial storage and computa...
Causality-inspired representation learning with spatiotemporal memory for polyp detection in endoscopic videos [0.03%]
受因果关系启发的结肠镜视频息肉检测的时空记忆表征学习方法
Zhuo Hu,Changjin Sun,Qi Zheng et al.
Zhuo Hu et al.
Early detection of colorectal polyps is crucial to reduce the morbidity and mortality associated with colorectal cancer. However, during endoscopy, continuous camera motion and the complex clinical environment often degrade key visual cues ...
Learning robust and task-invariant functional representation from fMRI through Siamese self-supervised learning [0.03%]
基于西雅美自我监督学习的鲁棒且任务不可见的功能性fMRI表征学习
Jiyao Wang,Peiyu Duan,Nicha C Dvornek et al.
Jiyao Wang et al.
Functional magnetic resonance imaging (fMRI) is a powerful tool for investigating human brain function. However, the high cost of data acquisition and the inherent subjectivity of psychiatric rating scales often lead to datasets with small ...
Patient-specific unsupervised neural reconstruction of cardiac blood flow from 4D flow MRI [0.03%]
基于4D流动磁共振的患者特异性无监督神经心脏血液流重建
Atharva Hans,Abhishek Singh,Yue-Hin Loke et al.
Atharva Hans et al.
Clinical 4D flow MRI can provide detailed measurements of cardiac blood flow, but current workflows rely on manual segmentation and post hoc velocity filtering, which introduce variability and reduce physical consistency. We introduce SMURF...
Breaking error coupling via divergent-convergent coordination for semi-supervised medical image segmentation [0.03%]
基于发散-收敛协调的半监督医学图像分割错误耦合破裂方法
Yuxuan Wan,Zhixuan Chen,Yuquan Xu et al.
Yuxuan Wan et al.
This study addresses the challenges of Error Coupling and model homogenization that commonly arise in dual-model collaborative learning for semi-supervised medical image segmentation by proposing a Divergent-Convergent Framework (DCF). The ...
GenAR: Next-scale autoregressive generation for spatial gene expression prediction [0.03%]
GenAR：空间基因表达预测的下一代自动回归生成模型
Jiarui Ouyang,Yihui Wang,Yihang Gao et al.
Jiarui Ouyang et al.
Spatial Transcriptomics (ST) offers spatially resolved gene expression but remains costly. Predicting expression directly from widely available Hematoxylin and Eosin (H&E) stained images presents a cost-effective alternative. However, most ...
Taming arbitrary modality missingness and imbalance: A unified graph-MoE framework for Alzheimer's disease diagnosis [0.03%]
处理任意模式的缺失和不平衡：一种用于阿尔茨海默病诊断的统一图-MoE框架
Guangqian Yang,Ye Du,Xiaowei Hu et al.
Guangqian Yang et al.
Multimodal biomarkers hold significant potential for improving Alzheimer's disease (AD) diagnosis. However, existing multimodal learning methods typically rely on idealized assumptions of complete and balanced modality availability, which r...
S2DENet: Shallow suppression and deep enhancement network for general ultrasound image segmentation [0.03%]
S2DENet：浅层抑制与深层增强的超声图像分割网络
Xintao Pang,Jinlin Yang,Zhifan Gao et al.
Xintao Pang et al.
Ultrasound image segmentation serves as a cornerstone of clinical diagnosis, yet remains a formidable challenge due to inherent image artifacts such as speckle noise and ambiguous boundaries. Existing approaches typically employ uniform fea...
Alzheimer's disease risk prediction via perceptual deformable attention generative adversarial network with large foundation models [0.03%]
基于大型基础模型的感知形变注意力生成对抗网络在阿尔茨海默病风险预测中的应用
Zhaoxu Xing,Zhengliang Liu,Da-Fang Zhang et al.
Zhaoxu Xing et al.
Predicting the risk of Alzheimer's disease (AD) is fundamental for early-stage intervention. Nevertheless, most methods struggle to extract multi-omics associative patterns due to the limited feature perception and inflexible disease modeli...