Mamba-enhanced disease semantic knowledge graph for interpretable automatic ICD coding [0.03%]
基于曼巴疾病的语义知识图的可解释自动ICD编码方法
Pengli Lu,Chao Dong,Jingjin Xue et al.
Pengli Lu et al.
Automatic ICD coding refers to the process of using artificial intelligence methods to automatically extract information related to diseases, symptoms, diagnoses, treatments, and other relevant details from electronic health records, and co...
Beyond manual transcripts: Exploring the potential of automatic speech recognition errors in improving Alzheimer's disease detection [0.03%]
超越手动转录：探索自动语音识别错误在改善阿尔茨海默病检测中的潜力
Yin-Long Liu,Yuanchao Li,Rui Feng et al.
Yin-Long Liu et al.
Objective: This study aims to extend the counterintuitive observation that Automatic Speech Recognition (ASR) errors can be beneficial for Alzheimer's Disease (AD) detection. Our objective is to conduct a large-scale inve...
Modeling temporal self and interactive evolution for biomedical hypothesis generation [0.03%]
基于时间自我的互动演化在生物医学假说生成中的应用研究模型
Hongyun Zeng,Huiwei Zhou,Weihong Yao et al.
Hongyun Zeng et al.
Objectives: Hypothesis generation (HG) aims to reveal meaningful hidden relationships between scientific terms from literature for accelerating innovation in drug discovery, disease prognosis and treatment. Recent studies...
Graph-spa: A Spatiotemporal Graph Neural Network based framework for ARDS prediction and interpretability [0.03%]
基于时空图神经网络的ARDS预测和可解释性框架GRAPH-SPA
Shashank Yadav,Molly Douglas,Jarrod Mosier et al.
Shashank Yadav et al.
Objective: Traditional deep learning models for multivariate time-series data often fall short in capturing long-range temporal dependencies critical for early prediction of the onset of acute respiratory distress syndrom...
CausalGenDiff: Generative causal diffusion bridges scRNA-seq and spatial transcriptomics [0.03%]
因果生成扩散模型：生成因果扩散桥接单细胞 RNA 测序和空间转录组学
Rabeya Tus Sadia,Md Atik Ahamed,Qiang Cheng
Rabeya Tus Sadia
Understanding gene expression within a spatial context requires the effective integration of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data. However, existing approaches often perform suboptimally, with structu...
Domain adaptation of stable diffusion for ultrasound inpainting: a synthetic data approach for enhanced thyroid nodule segmentation [0.03%]
基于稳定扩散的域适应超声图像插值技术在甲状腺结节分割中的应用研究
Antonin Prochazka,Jan Zeman
Antonin Prochazka
Objective: To enhance the cross-domain generalization of thyroid-nodule segmentation models by augmenting limited ultrasound training data with synthetic images generated by a fine-tuned Stable Diffusion model. ...
MDD-MARF: a multimodal depression detection model based on multi-level attention mechanism and residual fusion [0.03%]
基于多级注意机制和残差融合的多模态抑郁检测模型
Jianghai Zhou,Jike Ge,Zuqin Chen et al.
Jianghai Zhou et al.
Objective: Depression is a serious mental disorder that significantly affects patients' work ability and social functioning. With the rapid development of artificial intelligence, researchers have begun to explore automat...
Caption-augmented reasoning model with Hierarchical rank LoRA finetuing for medical visual question Answering [0.03%]
基于分层秩LoRA微调的 Caption增强推理模型在医学视觉问答中的应用
Yong Li,Jianping Man,Yi Zhou et al.
Yong Li et al.
Objective: Medical Visual Question Answering (VQA) is a quintessential application scenario of biomedical Multimodal Large Language Models (MLLMs). Previous studies mainly focused on input image-question pairs, neglecting...
Study on multimodal spatially-constrained contrastive learning for knee osteoarthritis severity grading [0.03%]
用于膝骨关节炎程度分级的多模态空间约束对比学习研究
YuHao Wu,Zhijie Xiang,Yuzhe Tan et al.
YuHao Wu et al.
To address the limitations of single-modal feature coverage and class distribution imbalance in knee osteoarthritis (KOA) classification, this study proposes a Multimodal Spatial-constraint Contrastive Learning (MSCL) model. First, dynamic ...
Multi-scale cancer driver gene prediction by flexible data selection and network topology guidance [0.03%]
基于灵活数据选择和网络拓扑引导的多尺度癌症驱动基因预测方法
Jian Liu,Yingzan Ren,Guodong Xiao et al.
Jian Liu et al.
Objective: Efficient and comprehensive prioritization of cancer driver genes across individual patients, cancer cohorts, and pan-cancer is crucial for advancing cancer diagnosis and treatment. The existing methods are eff...