Distilling Clinical Reasoning from Text Corpora for Explainable AI in Medical Imaging [0.03%]
基于文本语料的临床推理提取实现医学影像人工智能的可解释性
Lejun Fu,Zhongjian Wang,Tong Han et al.
Lejun Fu et al.
While deep learning models have achieved remarkable diagnostic accuracy in medical imaging, their inherent "black box" nature severely impedes clinical adoption due to a lack of transparency and trust. Current eXplainable AI (XAI) methods, ...
CRITIC-RAG: Knowledge-Augmented Large Language Models With Verified Retrieval for Improved Medical Reasoning [0.03%]
基于验证检索的增强大型语言模型在改进医学推理中的应用批评与展望(CRITIC-RAG)
Jin Li,Lei Lu,Yongming Miao et al.
Jin Li et al.
While retrieval-augmented generation (RAG) presents a promising solution for enhancing large language models (LLMs) in question answering (QA), particularly in knowledge-intensive domains like medicine, it continues to face challenges relat...
AISCT-SAM: Customized SAM-Med2D with 3D Context Awareness and Self-Prompt Generation for Fully Automatic Acute Ischemic Stroke Lesion Segmentation on Non-Contrast CT Scans [0.03%]
基于自适应上下文感知和自我提示生成的自动急性缺血性脑卒中病变分割方法AISCT-SAM
Hulin Kuang,Xianzhen Tan,Shunuo Li et al.
Hulin Kuang et al.
Lesion segmentation of acute ischemic stroke (AIS) patients on Non-Contrast CT (NCCT) scans plays a crucial role in rapid diagnosis and treatment planning. The direct application of promising semi-automatic SAM-Med2D to AIS lesion segmentat...
A Topology-Enhanced Dual-stream Attention Model for Parasternal Long-Axis View Identification in Echocardiography [0.03%]
一种拓扑增强的双流注意力模型，用于超声心动图下胸长轴视图识别
Shuo Gao,Zhong-Qing Shi,Shi-Yuan Qiao et al.
Shuo Gao et al.
The echocardiographic parasternal long-axis (PLAX) view serves as a fundamental reference for the comprehensive evaluation of the heart and its valves. However, subtle inter-view differences arising from the heart's complex anatomical struc...
Hand Gesture Intention Detection Using sEMG and Transfer Learning in Stroke Survivors [0.03%]
卒中患者利用sEMG和迁移学习进行手部动作意图识别
Maedeh Mohammadiazni,Krisztina Huszar,Sue Peters et al.
Maedeh Mohammadiazni et al.
Surface electromyography (sEMG) has shown potential for intuitive control of wearable assistive technologies in individuals with motor impairments. However, accurately classifying sEMG signals in stroke patients, particularly those with sev...
FewShotMetabolic: Parameter-Efficient Transfer Learning for Rapid Metabolic Risk Prediction in Data-Scarce Obesity Phenotypes [0.03%]
FewShotMetabolic：数据匮乏型肥胖表型中用于快速代谢风险预测的参数高效迁移学习方法
Shiqi Mo,Xiumei Wu,Huijun Qiu
Shiqi Mo
Creating risk prediction models based on obesity type requires large datasets, which are often not available for rare obesity phenotypes. Available technologies do not allow for consideration of the variability between different phenotypes,...
MPAN: A Multi-Prototype Adaptive Network for Few-Shot EEG-Based Biometric Recognition [0.03%]
基于EEG的生物认证中的few-shot多原型自适应网络(MPAN)方法研究
Jing Tang,Honggang Liu,Xuanyu Jin et al.
Jing Tang et al.
Biometric recognition based on electroencephalography (EEG), which captures intrinsic neural dynamics via scalp-recorded electrical activity, has shown great promise. Most existing studies leverage deep learning to enhance recognition perfo...
Altered Sensorimotor Lateralization and Cross-modal Consistency in Stroke: A Simultaneous EEG-fNIRS Study [0.03%]
脑卒中患者的感觉运动侧化及跨模态一致性改变：同步EEG-fNIRS研究
Sihong Wei,Mingfang Chen,Lianchi Huang et al.
Sihong Wei et al.
Stroke often leads to motor impairment and is associated with disruptions in sensorimotor pathways and altered cortical lateralization. Although understanding these changes is essential for interpreting post-stroke neural reorganization, it...
Dense Retrieval for Electronic Health Record With Knowledge Injection and Synthetic Data [0.03%]
知识注入和合成数据增强的电子健康记录密集检索
Zhengyun Zhao,Huaiyuan Ying,Sheng Yu
Zhengyun Zhao
Electronic Health Records (EHRs) are pivotal in clinical practices, yet their retrieval remains a challenge mainly due to semantic gap issues. Recent advancements in dense retrieval offer promising solutions but existing models, both genera...
Multi-Task Path-Based Heterogeneous Graph Model for Functional Brain Network Analysis and Gender-Related Diseases Diagnosis [0.03%]
基于路径的多任务异构图模型用于功能性脑网络分析及与性别相关的疾病诊断
Jiakun Xu,Ruiyan Fang,Tong Xiong et al.
Jiakun Xu et al.
Functional brain network analysis is crucial for understanding brain operating mechanism, aging, sexual distinction and brain disorders. As a powerful neuroimaging technique, resting-state functional Magnetic ResonanceImaging(rs-fMRI) measu...