Generative diffusion meets domain adaptation: a framework for EEG cross-subject motor imagery classification [0.03%]
生成扩散与领域适应：跨受试者EEG运动想象分类的框架
Jiacheng Zhang,Haolan Zhang,Youpeng Yang
Jiacheng Zhang
Cross-subject motor imagery classification remains challenging due to EEG data scarcity and inter-subject variability. This study proposes a novel framework integrating generative data augmentation with domain adaptation. First, we employ a...
Diffusion models for brain imaging computing: a survey of frameworks and applications [0.03%]
扩散模型在脑影像计算中的应用及框架综述
Yousuf Babiker M Osman,Aden Hassan Margani Elsanosi,Changhong Jing et al.
Yousuf Babiker M Osman et al.
Advances in brain imaging have generated unprecedented volumes of high-dimensional data, yet extracting meaningful information from complex, noisy, and incomplete brain imaging data remains a significant challenge. Diffusion models (DMs) ha...
Omid Hajilou,Howard Bowman
Omid Hajilou
In Rapid Serial Visual Presentation (RSVP), the vast majority of stimuli are not consciously perceived, but the salient ones breakthrough into awareness and can be reported. In addition, these breakthrough events are observable with EEG, si...
A deep hybrid CNN-BiLSTM-BiGRU architecture with explainability for mild cognitive impairment detection using EEG [0.03%]
一种用于使用EEG检测轻度认知障碍的具有可解释性的深度混合CNN-BiLSTM-BiGRU架构
Aishik Tokdar,Lakshya Agarwal,Shataghnee Chatterjee et al.
Aishik Tokdar et al.
Accurate detection of Mild Cognitive Impairment (MCI) is critical for timely intervention and for slowing progression to Alzheimer's disease. Electroencephalography (EEG) offers a non-invasive and cost-effective measure of brain activity; h...
A deep-learning framework for brain tumor segmentation via three-dimensional mass-preserving geometric transformation [0.03%]
基于三维保质几何变换的脑肿瘤分割深度学习框架
Tsung-Ming Huang,Kai-Qian Zheng,Wen-Wei Lin et al.
Tsung-Ming Huang et al.
This article presents a robust and efficient framework for brain tumor segmentation based on deep learning. We introduce a novel three-dimensional (3D) mass-preserving geometric transformation (MPGT) that employs a homotopy method to transf...
Benchmarking resting state fMRI connectivity pipelines for classification: robust accuracy despite processing variability in cross-site eye state prediction [0.03%]
休息状态功能磁共振连接流程在眼态预测中的稳健性及跨机构处理的变异性
Tatiana Medvedeva,Irina Knyazeva,Ruslan Masharipov et al.
Tatiana Medvedeva et al.
The rapid evolution of machine learning (ML) methods has yielded promising results in human brain neuroscience. However, the reproducibility of ML applications in neuroimaging remains limited, challenging the generalizability of inferences ...
Brain network classification considering directed propagation mechanisms of dynamic graphs [0.03%]
考虑动态图中图的定向传播机制的脑网络分类
Xinlei Wang,Zhongyang Wang,Keyan Cao
Xinlei Wang
The classification of functional brain networks plays an important role in the diagnosis of neurodegenerative diseases, brain decoding and other fields. Functional brain networks can effectively reflect the functional connection relationshi...
BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection [0.03%]
一种深度学习和XAI模型：通过理解MRI图像的局部、全局和顺序特征来提高脑肿瘤检测性能
Md Taimur Ahad,Bo Song,Yan Li
Md Taimur Ahad
The noise of Magnetic Resonance Imaging (MRI) poses challenges for Deep Learning (DL) when tumor boundaries are obscured, tumor location and appearance are complex due to overlap between tumor and non-tumor cells, and modality identificatio...
Anatomical-connectivity-guided functional connectivity reveals task-relevant pathways during proactive task-switching via recurrent graph neural networks [0.03%]
基于解剖连接的功能连接指导经由递归图神经网络的主动任务转换过程中的任务相关路径
Siyu Wang,Atsushi Miyata,Teruhisa Okuya et al.
Siyu Wang et al.
SegAnyNeuron: a neural image segmentation network with strong generalization performance by modeling image intensity variation [0.03%]
一种利用建模图像强度变化进行强泛化的神经图像分割网络：SegAnyNeuron
Lin Cai,Ying Zhang,Quanwei Ding et al.
Lin Cai et al.
Neuron reconstruction is a critical step in obtaining quantitative parameters of fine neuronal morphology from microscopic imaging data. Deep neural networks have been extensively applied in this field, with the predominant approach focusin...