Topology-constrained graph transformer network for structural and functional brain organization [0.03%]
拓扑约束图变换器网络在脑结构和功能组织中的应用
Jundan Ji,Mengjun Liu,Nanguang Chen et al.
Jundan Ji et al.
The human brain exhibits a complex and hierarchical organization that supports efficient information integration across local and global scales. Accurately characterizing such topological organization from neuroimaging data remains challeng...
UltraSoundNeRF: Sonographic neural reflection field for novel view synthesis [0.03%]
基于神经反射场的超声新型视角合成技术
Magdalena Wysocki,Mohammad Farid Azampour,Benjamin Busam et al.
Magdalena Wysocki et al.
Current state-of-the-art novel view synthesis methods generate natural scene images indistinguishable from real images. However, methods developed for ultrasound imaging often struggle with semantic accuracy, physical plausibility, or large...
Self-supervised reconstruction framework via motion- and physics-informed learning for four-dimensional magnetic resonance fingerprinting [0.03%]
基于运动和物理信息学习的四维磁共振指纹自监督重建框架
Chenyang Liu,Lu Wang,Xiang Wang et al.
Chenyang Liu et al.
Four-dimensional magnetic resonance fingerprinting (4DMRF) provides multi-parametric and motion-resolved tissue property quantification, promising to enhance the precision of liver cancer radiotherapy. However, its clinical translation is h...
MI2-Net: A Mamba-based network for joint incomplete multi-modal and incomplete label MRI image segmentation [0.03%]
基于蟒蛇的联合模态缺失及标签缺失MRI图像分割网络MI2-Net
Haotian Zhang,Shuaitong Zhang,Shichao Liang et al.
Haotian Zhang et al.
Current multi-modal segmentation methods typically rely on complete data and labels. However, clinical practice often faces the dual challenges of incomplete modalities and sparse annotations. Existing approaches mostly address these two is...
Teeth-GS: Gaussian Splatting Diffusion with enamel reflectance prior for single-image tooth crown reconstruction [0.03%]
基于齿釉质反射率的高斯点扩散单幅牙齿冠重建方法（Teeth-GS）
Yanxing Liang,Yinghui Wang,Wei Li et al.
Yanxing Liang et al.
High-fidelity 3D reconstruction of tooth crown surfaces is foundational to modern digital dentistry. Compared with intra-oral scanning (IOS) systems that require specialized hardware and may still miss fine occlusal structures under clinica...
Standardized evaluation of automatic methods for perivascular spaces segmentation in MRI - MICCAI 2024 challenge results [0.03%]
基于MRI的脑小血管周围间隙自动分割方法的标准化评价——MICCAI 2024挑战赛结果
Yilei Wu,Yichi Zhang,Zijian Dong et al.
Yilei Wu et al.
Perivascular spaces (PVS), when abnormally enlarged and visible in magnetic resonance imaging (MRI) structural sequences, are important imaging markers of cerebral small vessel disease and potential indicators of neurodegenerative condition...
Causal gradient intervention for debiased and evidence-grounded medical visual question answering [0.03%]
因果梯度干预在去偏见和基于证据的医学视觉问答中的应用
Bing Liu,Ziyuan Yang,Lijun Liu et al.
Bing Liu et al.
Medical Visual Question Answering (Med-VQA) aims to answer clinically relevant questions based on medical images. However, existing methods often struggle to provide visual evidence that is consistent with the query and verifiable. Under sc...
LungRes80: Towards tangled surgical workflow recognition in video-assisted thoracoscopic surgery [0.03%]
肺部手术识别：视频辅助胸腔镜手术中复杂手术流程识别的探索
Diandian Guo,Shu Yang,Jialun Pei et al.
Diandian Guo et al.
Video-Assisted Thoracoscopic Surgery (VATS) is a minimally invasive procedure developed to remove specific lung segments for the treatment of early-stage lung diseases. The surgical procedure involves intricate vascular and bronchial anatom...
KongNet: A multi-headed deep learning model for detection and classification of nuclei in histopathology images [0.03%]
基于组织病理学图像的细胞核检测与分类的多头深度学习模型 KongNet
Jiaqi Lv,Esha Sadia Nasir,Kesi Xu et al.
Jiaqi Lv et al.
Accurate detection and classification of nuclei in histopathology images are critical for diagnostic and research applications. We present KongNet, a multi-headed deep learning architecture featuring a shared encoder and parallel, cell-type...
Corrigendum to "Cooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping" [Medical Image Analysis 101 (2025) 103435] [0.03%]
“协同多任务学习和可解释的图像生物标志物在胶质瘤分级和分子亚型分类中的应用”的勘误表 [医学图像分析第101卷(2025年)103435号论文]
Qijian Chen,Lihui Wang,Zeyu Deng et al.
Qijian Chen et al.
Published Erratum
Medical image analysis. 2026 Jul 28:104238. DOI:10.1016/j.media.2026.104238 2026