UltraUNet: Real-Time Ultrasound Tongue Segmentation for Diverse Linguistic and Imaging Conditions [0.03%]
一种用于各种语言和影像条件下的实时超声舌分割方法.UltraUNet模型
Alisher Myrgyyassov,Zhen Song,Yu Sun et al.
Alisher Myrgyyassov et al.
Ultrasound tongue imaging (UTI) provides a non-invasive, cost-effective modality for investigating speech articulation, speech motor control, and speech-related disorders. However, real-time tongue contour segmentation remains a significant...
A Segmentation-Guided Feature Alignment and Fusion Network for Glioma IDH Genotyping [0.03%]
一种基于分割引导特征对齐和融合的胶质瘤IDH分型网络
Minghui Chen,Guohua Zhao,Lei Yang et al.
Minghui Chen et al.
Isocitrate dehydrogenase (IDH) is a pivotal molecular marker for glioma diagnosis, prognosis, and treatment planning. Multi-modal deep learning methods, which integrate features from multiple magnetic resonance imaging (MRI) sequences, have...
Direct PET-to-CT Generation for Attenuation Correction: A Slice-to-Slice Continual Transformer Segmentation-Aware Network [0.03%]
一种基于连续变换器分割感知网络的直接PET到CT生成方法用于衰减校正研究
Rongjun Ge,Hanyuan Zheng,Yuxin Liu et al.
Rongjun Ge et al.
Direct synthetic computed tomography (CT) generation from positron emission tomography (PET) plays a crucial role in PET attenuation correction, yet providing detailed structural information to compensate for functional imaging. Compared to...
MSFSNet: Multi-Source Few-Shot Adaptation Network for Cross-Subject Depression Recognition from EEG Signals [0.03%]
基于EEG信号的多源Few-shot适应网络抑郁跨被试识别模型蒋文悦
Kang Wang,Yanan Zhang,Yingwei Zhang et al.
Kang Wang et al.
Depression is a prevalent mental disorder with severe socio-economic implications, and its early identification and intervention are crucial for mitigating disease progression. However, existing machine learning and deep learning-based appr...
Harnessing Terminal Signal-Aware Deep Learning for Accurate Multi-Class Secreted Effector Identification [0.03%]
利用终端信号感知的深度学习进行精确的分泌效应子多分类识别
Lesong Wei,Shida He,Quan Zou et al.
Lesong Wei et al.
Gram-negative bacterial secreted effectors are translocated through specialized secretion systems to manipulate host cellular processes, and their accurate identification is crucial for understanding bacterial pathogenesis. Recent deep lear...
Movement Anywhere: An Open-Source Distributed 2D Video-Based Movement Analysis Platform Empowered by Active Learning [0.03%]
无处不运动：一种基于主动学习的开源分布式二维视频行为分析平台
Ming-Yang Ho,Yufeng Jane Tseng
Ming-Yang Ho
Movement analysis plays a pivotal role in diagnosing and monitoring neurodegenerative and musculoskeletal diseases. Traditional tools, such as 3D motion capture systems and electronic walkways, though effective, are costly and spatially dem...
Explainable Liquid Time-Constant Network for Multi-Modal Fatigue Detection in Healthcare 4.0 [0.03%]
用于医疗4.0多模态疲劳检测的可解释液态时间常数网络
Xu Xu,Ghulam Muhammad
Xu Xu
Driver fatigue detection has become increasingly critical for healthcare 4.0 systems, as it enables real-time monitoring of internal cognitive states to ensure road safety. However, existing methods often suffer from two critical limitation...
FedTFT: Federated Temporal Fusion Transformer for Interpretable Multi-Horizon Psychiatric Risk Prediction Across Cross-Silo Hospitals [0.03%]
联邦时序融合变换器：跨机构精神疾病风险预测的解释性多步预测模型
Akeel Ahamed,Ri-Ra Kang,KangYoon Lee
Akeel Ahamed
Psychiatric inpatient monitoring generates multimodal data, but privacy constraints and cross-hospital heterogeneity limit centralized learning. We propose FedTFT, a federated Temporal Fusion Transformer for multi-horizon psychiatric risk p...
Medical Knowledge-Driven Contrastive Learning for Similar Patient Retrieval [0.03%]
一种基于医学知识的对比学习的相似患者检索方法
Fanqing Meng,Chong Feng,Ge Shi et al.
Fanqing Meng et al.
Similar patient retrieval is a fundamental task in medical informatics, aiming to identify patients with similar clinical characteristics to assist in diagnosis and treatment plan recommendation. While traditional methods relying on lexical...
Wavelet-Transformer Attention Network for Accurate Fetal ECG Estimation from Multi-Channel Abdominal Signals [0.03%]
基于多通道腹部分段信号的准确胎儿心电估测_wavelet-transformer注意力网络
Xu Wang,Zhaoshui He,Zhijie Lin et al.
Xu Wang et al.
Accurate fetal electrocardiogram extraction from abdominal recordings remains challenging due to strong maternal electrocardiogram artifacts and low signal quality. To address these issues, a Wavelet-Transformer Attention Network (WTA-Net) ...