Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine [0.03%]
一种高效的梯度提升算法实现的联邦函数回归在隐私保护远程医疗中的应用
Yu Ding,Carlos Costa,Bing Si
Yu Ding
Federated Learning (FL) is an emerging computing paradigm to collaboratively train Machine Learning (ML) models across multi-source data while preserving privacy. The major challenge of "meaningful" implementation of FL for any ML model is ...
MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records [0.03%]
具有缺失值意识的多重分支自注意力编码器MUSE-Net用于不规则纵向电子健康记录
Zekai Wang,Tieming Liu,Bing Yao
Zekai Wang
The era of big data has made vast amounts of clinical data readily available, particularly in the form of electronic health records (EHRs), which provides unprecedented opportunities for developing data-driven diagnostic tools to enhance cl...
Development and Quantitative Evaluation of a Novel Autonomous In situ Bioprinting Surgical Robotic Framework For Treatment of Volumetric Muscle Loss Injuries [0.03%]
一种新型自主生物墨水原位生物打印手术机器人框架的开发与定量评估用于治疗体积性肌肉损失损伤
Shuojue Yang,Hansoul Kim,Omid Rezayof et al.
Shuojue Yang et al.
In situ bioprinting has been identified as a promising tissue engineering technique for treating volumetric muscle loss (VML) injuries. However, the success of this procedure significantly depends on the uniform and precise deposition of ce...
A Novel Hybrid Ordinal Learning Model with Health Care Application [0.03%]
一种新颖的序数混合模型及其在医疗保健领域的应用研究
Lujia Wang,Hairong Wang,Yi Su et al.
Lujia Wang et al.
Ordinal learning (OL) is a type of machine learning models with broad utility in health care applications such as diagnosis of different grades of a disease (e.g., mild, modest, severe) and prediction of the speed of disease progression (e....
Distribution-agnostic Probabilistic Few-shot Learning for Multimodal Recognition and Prediction [0.03%]
一种用于多模态识别和预测的分布无关的统计性元学习方法
Di Wang,Xiaochen Xian,Haidong Li et al.
Di Wang et al.
In industrial scenarios with insufficient sensor data, intelligent few-shot failure mode recognition and remaining useful lifetime (RUL) prediction are critically essential for effective prognostics and health management. Existing few-shot ...
Oral-Anatomical Knowledge-Informed Semi-Supervised Learning for 3D Dental CBCT Segmentation and Lesion Detection [0.03%]
基于口腔解剖知识的半监督学习在三维牙科CBCT图像分割和病变检测中的应用研究
Yeonju Lee,Min Gu Kwak,Rui Qi Chen et al.
Yeonju Lee et al.
Cone beam computed tomography (CBCT) is a widely-used imaging modality in dental healthcare. It is an important task to segment each 3D CBCT image, which involves labeling lesions, bone, teeth, and restorative material on a voxel-by-voxel b...
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review [0.03%]
基于知识的机器学习在癌症诊断和预后中的应用：综述
Lingchao Mao,Hairong Wang,Leland S Hu et al.
Lingchao Mao et al.
Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning (ML) has enabled in-depth analysis of complex patterns from large, diverse datasets, greatly facilitating "healthcare automation" in cancer ...
A Cross-Modal Mutual Knowledge Distillation Framework for Alzheimer's Disease Diagnosis: Addressing Incomplete Modalities [0.03%]
一种针对阿尔茨海默病诊断的跨模态相互知识蒸馏框架：解决模态不完整问题
Min Gu Kwak,Lingchao Mao,Zhiyang Zheng et al.
Min Gu Kwak et al.
Early detection of Alzheimer's Disease (AD) is crucial for timely interventions and optimizing treatment outcomes. Integrating multimodal neuroimaging datasets can enhance the early detection of AD. However, models must address the challeng...
Weakly Supervised Deep Learning for Monitoring Sleep Apnea Severity Using Coarse-grained Labels [0.03%]
基于粗粒度标签的睡眠呼吸暂停严重程度监测的弱监督深度学习方法
Xin Zan,Di Wang,Changyue Song et al.
Xin Zan et al.
Sleep apnea, a prevalent sleep-related breathing disorder, often remains undiagnosed and untreated in a large patient population due to the need of extensive manual annotations on various physiological signals for clinical diagnosis. Despit...
Weakly-Supervised Transfer Learning with Application in Precision Medicine [0.03%]
基于弱监督迁移学习的精准医疗应用研究
Lingchao Mao,Lujia Wang,Leland S Hu et al.
Lingchao Mao et al.
Precision medicine aims to provide diagnosis and treatment accounting for individual differences. To develop machine learning models in support of precision medicine, personalized models are expected to have better performance than one-mode...