A Language-Guided Progressive Fusion Network with semantic density alignment for medical visual question answering [0.03%]
基于语义密度对齐的语言引导渐进融合网络在医疗视觉问答中的应用研究
Shuxian Du,Shuang Liang,Yu Gu
Shuxian Du
Medical Visual Question Answering (Med-VQA) is a critical multimodal task with the potential to address the scarcity and imbalance of medical resources. However, most existing studies overlook the limitations of the inconsistency in informa...
A novel data-driven approach for Personas validation in healthcare using self-supervised machine learning [0.03%]
利用自我监督机器学习在医疗保健中验证人物原型的新数据驱动方法
Emanuele Tauro,Alessandra Gorini,Grzegorz Bilo et al.
Emanuele Tauro et al.
Objective: Persona validation is a challenging task, often relying on costly external validation methods. The aim of this study was the development of a novel method for Personas validation based on data already available...
Tentative renderings: Describing local data infrastructures that support the implementation and evaluation of national evaluation Initiatives [0.03%]
初步渲染：描述支持国家评估计划的实施和评估的地方数据基础设施
Jennifer Van Tiem,Nicole L Johnson,Erin Balkenende et al.
Jennifer Van Tiem et al.
Objective: Data journeys are a way to describe and interrogate "the life of data" (Bates et al 2010). Thus far, they have been used to clarify the mobile nature of data by visualizing the pathways made by handling and mov...
MedicalGLM: A Pediatric Medical Question Answering Model with a quality evaluation mechanism [0.03%]
MedicalGLM：具备质量评估机制的儿科医疗问答模型
Xin Wang,Zhaocai Sun,Pingping Wang et al.
Xin Wang et al.
Objective: Large Language models (LLMs) have a wide range of medical applications, especially in scenarios such as question-answering. However, existing models face the challenge of accurately assessing the quality of inf...
FedIMPUTE: Privacy-preserving missing value imputation for multi-site heterogeneous electronic health records [0.03%]
FedIMPUTE：多站点异构电子健康记录的隐私保护缺失值插补
Siqi Li,Mengying Yan,Ruizhi Yuan et al.
Siqi Li et al.
Objective: We propose FedIMPUTE, a communication-efficient federated learning (FL) based approach for missing value imputation (MVI). Our method enables multiple sites to collaboratively perform MVI in a privacy-preservin...
Enhancing generalization of medical image segmentation via game theory-based domain selection [0.03%]
通过基于博弈论的选择领域来提高医学图像分割的泛化能力
Zuyu Zhang,Yan Li,Byeong-Seok Shin
Zuyu Zhang
Medical image segmentation models often fail to generalize well to new datasets due to substantial variability in imaging conditions, anatomical differences, and patient demographics. Conventional domain generalization (DG) methods focus on...
Contextual information contributes to biomedical named entity normalization [0.03%]
上下文信息有助于生物医学命名实体规范化的实现
Gengxin Luo,Nannan Shi,Gang Wang et al.
Gengxin Luo et al.
Objective: As one of the most crucial upstream tasks in biomedical informatics, biomedical named entity normalization (BNEN) aims to map mentioned named entities to uniform standard identifiers or terms. Most existing met...
ieGENES: A machine learning method for selecting differentially expressed genes in cancer studies [0.03%]
ieGENES：癌症研究中选择差异表达基因的机器学习方法
Xiao-Lei Xia,Shang-Ming Zhou,Yunguang Liu et al.
Xiao-Lei Xia et al.
Gene selection is crucial for cancer classification using microarray data. In the interests of improving cancer classification accuracy, in this paper, we developed a new wrapper method called ieGENES for gene selection. First we proposed a...
CECRel: A joint entity and relation extraction model for Chinese electronic medical records of coronary angiography via contrastive learning [0.03%]
CECRel：通过对比学习进行冠状动脉造影中文电子病历的实体和关系联合抽取模型
Yetao Tong,Jijun Tong,Shudong Xia et al.
Yetao Tong et al.
Entity and relation extraction from Chinese electronic medical records (EMRs) is a crucial foundation for constructing medical knowledge graphs and supporting downstream tasks. Chinese EMRs face challenges in accurately extracting medical e...
Network-based analysis of Alzheimer's Disease genes using multi-omics network integration with graph diffusion [0.03%]
基于多组学网络整合与图扩散的阿尔茨海默病基因的网络分析
Softya Sebastian,Swarup Roy,Jugal Kalita
Softya Sebastian
Alzheimer's Disease (AD) is a complex neurodegenerative disorder affecting millions worldwide. Despite extensive research, the mechanisms behind AD remain elusive. Many studies suggest that disease-responsible genes often act as hub genes i...