Comparative Screening of Alzheimer's Disease, Lewy Body Dementia, and Frontotemporal Dementia Using miRNA and Machine Learning [0.03%]
基于miRNA和机器学习的阿尔茨海默病、路易体痴呆和额颞叶痴呆比较筛查研究
Benjamin Luo,Valentina L Kouznetsova,Igor F Tsigelny
Benjamin Luo
Current dementia diagnostic methods can be costly, invasive, or limited in their ability to distinguish between disorders with overlapping clinical symptoms. Dysregulated microRNAs (miRNAs) have emerged as promising noninvasive biomarkers f...
Progressive Disambiguation and Sycophancy Mitigation for Prompt Uncertainty in Generative AI [0.03%]
生成式AI中的渐进式歧义消解和谄媚缓解技术以应对提示不确定性
Fabrizio Marozzo,Loris Belcastro
Fabrizio Marozzo
Generative AI systems increasingly support users in coding, data analysis, and creative tasks through natural-language interaction. However, user prompts are often underspecified or ambiguous, and current LLM-based assistants typically proc...
Unified Multi-Class Electroencephalogram Artifact Recognition Using Machine Learning Classifiers [0.03%]
基于机器学习分类器的统一多类脑电图伪迹识别方法
Fernando Moncada Martins,Ramón Suárez,José R Villar et al.
Fernando Moncada Martins et al.
Artifacts are noisy signals that commonly contaminate electroencephalographic (EEG) recordings, mixing with underlying brain activity and degrading the quality of neurophysiological data. Previous research on epileptic Anomaly Detection has...
Soner Kotan,Aydin Akan
Soner Kotan
Deep learning (DL) has shown considerable promise for EEG-based dementia assessment; however, rigorous cross-family comparisons under leakage-free and clinically meaningful evaluation protocols remain limited. To address this gap, we benchm...
Toward Efficient and Generalizable Text Dataset Distillation via a Dual-Agent Large Language Model Framework [0.03%]
基于双智能体大型语言模型框架的高效通用文本数据集提取方法研究
Junhai Zhou,Zhongfeng Wang,Meiqi Wang
Junhai Zhou
Large-scale datasets impose substantial training costs on machine learning models. Dataset distillation addresses this issue by synthesizing compact datasets that can achieve performance comparable to that of the original data. However, tex...
Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders [0.03%]
潜空间投影和地图集使用自动编码器进行深度神经影像的一则警示故事
J M Gorriz,F Segovia,C Jimenez-Mesa et al.
J M Gorriz et al.
This study introduces a deep learning framework for the inferential exploration of latent representations in 3D brain MRI, leveraging a simple convolutional autoencoder with a hierarchical encoder and a compact latent space. Trained on segm...
Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images [0.03%]
基于Transformer的异常检测在MRI图像中进行神经退行性筛查
Enol García González,Mădălina Dicu,José R Villar et al.
Enol García González et al.
The automatic detection of anomalies in medical images is a significant challenge in the assisted diagnosis of neurodegenerative diseases such as Alzheimer's. This paper presents an anomaly detection model based on Transformers for the anal...
Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction [0.03%]
离散小波卷积的时间频率学习表示及其在癫痫发作预测中的应用
Weisen Lu,Haotian Li,Guoyang Liu et al.
Weisen Lu et al.
Accurate and adaptive time-frequency representation is essential for analyzing nonstationary signals in critical applications, such as epileptic seizure prediction utilizing electroencephalogram (EEG) data. However, existing deep learning a...
Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer [0.03%]
基于不平衡感知变压器的分层频谱时序特征学习的自动癫痫发作检测方法
Zuyi Yu,Yang Li
Zuyi Yu
Epilepsy manifests as a chronic neurological condition marked by recurrent seizures. Recent advances in computational analysis of Electroencephalography (EEG) signals have enabled new possibilities for identifying ictal events in extended r...
Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals [0.03%]
基于多通道EEG信号的金字塔视觉变压器增强型Conformer网络癫痫发作识别方法
Weiguang Dong,Jian Lian,Xinyu Wang et al.
Weiguang Dong et al.
The classification of electroencephalogram (EEG) signals plays an important role in neuroscience research and clinical diagnosis of epileptic seizures. This work aims to solve EEG data classification tasks by using the Children's Hospital B...