Enhancing feature fusion of U-like networks with dynamic skip connections [0.03%]
基于动态跳跃连接的U_like网络特征融合增强方法
Yue Cao,Quansong He,Kaishen Wang et al.
Yue Cao et al.
U-like networks have become fundamental frameworks in medical image segmentation through skip connections that bridge high-level semantics and low-level spatial details. Despite their success, conventional skip connections exhibit two key l...
PH2ST: Prompt-guided hypergraph learning for spatial transcriptomics prediction in whole slide images [0.03%]
基于提示的超图学习在全片扫描图像中进行空间转录组预测
Yi Niu,Jiashuai Liu,Yingkang Zhan et al.
Yi Niu et al.
Spatial Transcriptomics (ST) reveals the spatial distribution of gene expression in tissues, offering critical insights into biological processes and disease mechanisms. However, the high cost, limited coverage, and technical complexity of ...
Robust fine-grained echocardiographic view classification with supervised contrastive learning [0.03%]
具有监督对比学习的鲁棒精细粒度心超图像分类方法
Preshen Naidoo,Patricia Fernandes,Nasim Dadashi Serej et al.
Preshen Naidoo et al.
Accurate classification of echocardiographic views is fundamental for automated cardiac analysis. However, clinical practice relies on a large, heterogeneous set of fine-grained acquisitions that introduce substantial inter-observer variabi...
Dual selective gleason pattern-aware multiple instance learning with uncertainty regularization for grade group prediction in histopathology images [0.03%]
基于不确定性正则化的双选择gleason图谱感知的多实例学习在组织病理学图像分级分组预测中的应用
Xinyu Hao,Hongming Xu,Jingdong Zhang et al.
Xinyu Hao et al.
Accurate prediction of Gleason Grade Group (GG) is of great importance for prostate cancer risk stratification and treatment planning. Although multiple instance learning (MIL) methods have advanced Gleason grading, most existing studies ov...
Chuanhao Zhang,Yangxi Li,Jianping Song et al.
Chuanhao Zhang et al.
Optical coherence tomography (OCT) A-scan backscattering signals provide depth-resolved textural information about internal structures. However, conventional OCT imaging is limited by refraction-induced distortion and speckle noise, hinderi...
Enhancing uncertainty assessment in dynamic PET imaging with residual permutation and clustering [0.03%]
基于剩余置换和聚类的动态PET成像不确定性评估增强方法
Kun Ma,Fangxiao Cheng,Wei Liu et al.
Kun Ma et al.
Quantitative positron emission tomography (PET) is widely used for disease diagnosis and therapy monitoring, yet the reliability of kinetic parameters depends on robust uncertainty quantification. Existing Bayesian methods are computational...
Unsupervised domain adaptation for medical image segmentation using adaptogen-perturbation [0.03%]
基于适应生成扰动的医学图像分割的无监督领域自适应方法
Hong Joo Lee,Yuan Bi,Sangmin Lee et al.
Hong Joo Lee et al.
Domains shift originated from differences in devices or patients in the medical field, poses a significant challenge when applying pre-trained models to clinical applications. To tackle this challenge, domain adaptation methods have been ex...
Incorporating global-local tissue changes to predict future breast cancer from longitudinal screening mammograms [0.03%]
结合纵向筛查乳腺X线摄影的全局和局部组织变化预测未来乳腺癌风险
Xin Wang,Tao Tan,Yuan Gao et al.
Xin Wang et al.
Early detection of breast cancer (BC) through mammography screening is critical for reducing mortality and improving patient outcomes. However, full-population-based, age-driven screening might not lead to optimal resource use and may enlar...
MICCAI STS 2024 challenge: Semi-supervised instance-level tooth segmentation in panoramic X-ray and CBCT images [0.03%]
MICCAI STS 2024挑战赛：全景X光和CBCT图像中半监督下的牙实例分割
Yaqi Wang,Zhi Li,Chengyu Wu et al.
Yaqi Wang et al.
Orthopantomogram (OPGs) and Cone-Beam Computed Tomography (CBCT) are vital for dentistry, but creating large datasets for automated tooth segmentation is hindered by the labor-intensive process of manual instance-level annotation. This rese...
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...