Improving organizational processes in healthcare through simulation-driven resource allocation: Methodology and real-world case study [0.03%]
基于模拟的资源分配改善医疗保健组织过程：方法与真实世界案例研究
Francesco Vinci,Davide Aloini,Elisabetta Benevento et al.
Francesco Vinci et al.
The global rise in the aging population presents significant challenges to healthcare systems worldwide, which thus need more efficiency and effectiveness. Healthcare systems deliver services through processes that encode (inter)national re...
A multi-feature alignment fusion neural network model for red blood cell aggregation classification using ultrasonic radiofrequency data of blood [0.03%]
基于血流射频数据的红细胞聚类融合神经网络模型研究
Jinsong Guo,Yufeng Zhang,Bingbing He et al.
Jinsong Guo et al.
Evaluation of red blood cell (RBC) aggregation is crucial for the early prevention and accurate diagnosis of diseases such as ischemic cardiovascular disease, type II diabetes mellitus, and sickle cell disease. Ultrasound technology is wide...
Precise estimation of tissue microstructure with hybrid graph transformer [0.03%]
基于混合图变换器的组织微结构精确实体化
Haotian Jiang,Geng Chen,Jiquan Ma et al.
Haotian Jiang et al.
The accurate estimation of tissue microstructure requires a sufficient amount of Diffusion MRI (DMRI) data, however, the clinical acquisition of this is challenging. Deep learning therefore improves the inference of tissue microstructure by...
A systematic review of machine and deep learning techniques for acute lymphoblastic leukemia diagnosis [0.03%]
急性淋巴细胞白血病诊断的机器和深度学习技术的系统回顾
W Hussain Shah,S Rafia Fatima,R Jaimes-Reátegui et al.
W Hussain Shah et al.
Acute lymphoblastic leukemia (ALL) is a hematological malignancy characterized by the rapid proliferation of immature white blood cells in the bone marrow. Early and accurate diagnosis is essential for improving clinical outcomes; however, ...
Twin cross contrastive learning with multi-modality fusion for drug-target affinity prediction [0.03%]
基于多模态融合的孪生交叉对比学习药物-靶点亲和力预测
Linna Zhang,Zhaowei Wang,Wuhao Liu et al.
Linna Zhang et al.
Accurate prediction of drug-target binding affinity (DTA) can provide valuable insights for accelerating drug discovery and repositioning. While deep learning has demonstrated remarkable progress in facilitating DTA prediction, most existin...
Syndrome differentiation of Traditional Chinese Medicine via multiple knowledge enhancement with Kolmogorov-Arnold Theorem [0.03%]
基于Kolmogorov-Arnold定理的中医药辨证知识增强方法研究
Yi Yang,Xuxiang Lu,Wenrong An et al.
Yi Yang et al.
Traditional Chinese Medicine (TCM) plays an important role in global medical practices. Syndrome differentiation (SD) is a key step in the diagnosis and treatment of TCM, which involves a comprehensive analysis of patient clinical informati...
EvoPS: Evolutionary Patch Selection in the Training Embedding Space of Whole Slide Images [0.03%]
基于整个幻灯片图像训练嵌入空间的进化补丁选择(EvoPS)
Saya Hashemian,Azam Asilian Bidgoli
Saya Hashemian
In computational pathology, the gigapixel scale of Whole-Slide Images (WSIs) requires their decomposition into thousands of patches, resulting in high-dimensional embeddings that are computationally costly to process and often dominated by ...
CL-MHAD: Contrastive Learning-based Multi-Hypergraph Aggregation and Diffusion model for prescription recommendation [0.03%]
基于对比学习的多超图聚合和扩散模型的处方推荐方法
Juanzi Zhou,Yin Zhang,Fang Hu et al.
Juanzi Zhou et al.
Multiple syndrome-based prescription recommendations are significant for personalized diagnosis and treatment in Traditional Chinese Medicine (TCM). However, it remains a challenge to effectively extract and fuse multi-dimensional knowledge...
TADynFed: Dynamic modality-adaptive federated learning with tissue-aware disentanglement for cross-disease analysis [0.03%]
基于组织感知解缠的动态模态自适应联邦学习跨疾病分析(TADynFed)
Saeed Iqbal,Xiaopin Zhong,Muhammad Attique Khan et al.
Saeed Iqbal et al.
Federated learning (FL) enables collaborative medical image analysis across decentralized institutions while preserving data privacy. However, real-world deployment faces critical challenges: modality heterogeneity, where clients possess in...
GFASNet: Gait feature attention-driven deep sequential network for dementia-related gait pattern analysis [0.03%]
GFASNet：与步态模式分析相关的痴呆症深度序列网络的步态特征注意力机制
Quynh Hoang Ngan Nguyen,Ankhzaya Jamsrandorj,Dawoon Jung et al.
Quynh Hoang Ngan Nguyen et al.
Deep learning models leveraging human activity data, such as gait, have shown promise for dementia prediction. However, their limited interpretability and lack of clinically meaningful insights restrict their translational value in cognitiv...