找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023; 26th International C Hayit Greenspan,Anant Madabhushi,Russell Tay

[复制链接]
楼主: 多愁善感
发表于 2025-3-25 05:49:50 | 显示全部楼层
发表于 2025-3-25 09:28:58 | 显示全部楼层
发表于 2025-3-25 15:41:34 | 显示全部楼层
Flow-Based Geometric Interpolation of Fiber Orientation Distribution FunctionsHowever, the complicated mathematical structures of the FOD function pose challenges for FOD image processing tasks such as interpolation, which plays a critical role in the propagation of fiber tracts in tractography. In FOD-based tractography, linear interpolation is commonly used for numerical ef
发表于 2025-3-25 19:32:28 | 显示全部楼层
Learnable Subdivision Graph Neural Network for Functional Brain Network Analysis and Interpretable Cnct brain states can confer heterogeneous functions to brain networks. Recent studies have revealed that extracting information from functional brain networks is beneficial for neuroscience analysis and brain disorder diagnosis. Graph neural networks (GNNs) have been demonstrated to be superior in l
发表于 2025-3-25 19:58:34 | 显示全部楼层
发表于 2025-3-26 00:52:47 | 显示全部楼层
发表于 2025-3-26 06:04:42 | 显示全部楼层
Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model Contrast Agents (GBCAs). These DL algorithms are however limited by the availability of high quality low dose datasets. Additionally, different types of GBCAs and pathologies require different dose levels for the DL algorithms to work reliably. In this work, we formulate a novel transformer (Gforme
发表于 2025-3-26 08:46:14 | 显示全部楼层
Development and Fast Transferring of General Connectivity-Based Diagnosis Model to New Brain Disordeire training new models with large data from new BDs, which is often not practical. Recent neuroscience studies suggested that BDs could share commonness from the perspective of functional connectivity derived from fMRI. This potentially enables developing a connectivity-based general model that can
发表于 2025-3-26 16:42:33 | 显示全部楼层
发表于 2025-3-26 18:32:00 | 显示全部楼层
Dynamic Structural Brain Network Construction by Hierarchical Prototype Embedding GCN Using T1-MRI. Current methods with T1-MRI rely on predefined regions or isolated pretrained modules to localize atrophy regions, which neglects individual specificity. Besides, existing methods capture global structural context only on the whole-image-level, which weaken correlation between regions and the hier
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-23 00:02
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表