Magnetic resonance image enhancement and segmentation using conventional and deep learning denoising techniques for dynamic cerebral angiography [0.03%]
基于常规和深度学习去噪技术的磁共振血管成像图像增强与分割
Daniela Herrera,Gilberto Ochoa-Ruiz,Christian Stephan-Otto et al.
Daniela Herrera et al.
The study of brain vascular dynamic patterns in infants, through dynamic angio MRI (TRANCE-MRI) images, is relevant to identify pathologies associated with brain flow and perfusion. However, several drawbacks arise while using these types o...
Construction of performance score dynamic prediction system for clinical departments using explainable machine learning [0.03%]
基于可解释机器学习的科室绩效评分动态预测系统构建
Huashu Wen,Xiaohua Li,Haibo Zhang et al.
Huashu Wen et al.
Background: Accurate evaluation of clinical departmental performance is essential for public hospital management. However, existing approaches primarily rely on static, retrospective annual assessments and lack interpreta...
CURENet: combining unified representations for efficient chronic disease prediction [0.03%]
结合统一表示的慢性病预测模型CURENet
Cong-Tinh Dao,Nguyen Minh Thao Phan,Jun-En Ding et al.
Cong-Tinh Dao et al.
Electronic health records (EHRs) are designed to synthesize diverse data types, including unstructured clinical notes, structured lab tests, and time-series visit data. Physicians draw on these multimodal and temporal sources of EHR data to...
Reasoning with large language models in medicine: a systematic review of techniques, challenges and clinical integration [0.03%]
大型语言模型在医学中的推理：技术、挑战和临床整合的系统回顾
Isra Mansoor,Muhammad Abdullah,Muhammad Dawood Rizwan et al.
Isra Mansoor et al.
Large Language Models (LLMs) have emerged as transformative tools in healthcare, demonstrating unprecedented capabilities in medical reasoning tasks that require complex inference, pattern recognition, and decision-making under uncertainty....
A multimodal approach for cardiac signals classification using deep learning with explainable AI methods [0.03%]
基于可解释人工智能方法的深度学习在心脏信号分类中的多模态应用研究
Ali Mohammad Alqudah,Ausilah Alfraihat
Ali Mohammad Alqudah
Cardiovascular diseases remain a leading cause of mortality worldwide, necessitating accurate and timely diagnosis. Electrocardiogram (ECG) and phonocardiogram (PCG) signals provide complementary information about cardiac function, electric...
Text-based prediction of ımmunohistochemical biomarkers in breast cancer using a generative large language model: a retrospective study [0.03%]
基于文本预测乳腺癌免疫组化生物标志物：一项回顾性研究
Emre Utkan Büyükceran,Ayça Seyfettin,Andelib Babatürk et al.
Emre Utkan Büyükceran et al.
Purpose: Immunohistochemical (IHC) biomarkers such as estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67 are essential for the classification and treatment of breast cancer. While radiomics-based models h...
Vaner2: towards more general biomedical named entity recognition using multi-task large language model encoders [0.03%]
Vaner2：使用多任务大型语言模型编码器实现更通用的生物医学命名实体识别
Yuxuan Liu,Junyi Bian,Weiqi Zhai et al.
Yuxuan Liu et al.
Biomedical named entity recognition (BioNER) serves as the foundation for many downstream tasks, such as relation extraction, question answering, and clinical text analysis. BioNER was previously dominated by BERT-based models pretrained on...
Large language models in healthcare: a systematic evaluation on medical Q/A datasets [0.03%]
大型语言模型在医疗保健中的应用：基于医学问答数据集的系统评估
Khuzaima Tofeeq,Asma Naseer,Aamir Wali
Khuzaima Tofeeq
This study systematically evaluates the performance of state-of-the-art large language models (LLMs) in medical and healthcare applications, focusing on their accuracy in answering domain-specific questions. Using benchmark medical question...
Deep continual multitask out-of-hospital incident severity assessment from changing clinical features [0.03%]
基于临床特征变化的深度连续多任务院外事件严重程度评估
Pablo Ferri,Carlos Sáez,Antonio Félix-De Castro et al.
Pablo Ferri et al.
Purpose: When developing machine learning models to support emergency medical triage, it is important to consider how changes over time in the input features can negatively affect the models' performance. The objective of...
MOLiNAS: multi-objective lightweight neural architecture search for whole-slide multi-class blood cell segmentation [0.03%]
MOLiNAS：用于全片多类血液细胞分割的轻量级神经结构多目标搜索
Zeki Kuş,Berna Kiraz,Musa Aydin et al.
Zeki Kuş et al.
Blood cell analysis plays a key role in clinical diagnosis and hematological research. The accurate identification and quantification of different blood cell types is essential for the diagnosis of various diseases. The conventional manual ...