A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data [0.03%]
基于电子健康记录分类数据的自监督表示学习在临床决策中的应用现状研究综述
Zheng Yuanyuan,Bensahla Adel,Bjelogrlic Mina et al.
Zheng Yuanyuan et al.
The widespread adoption of Electronic Health Records (EHRs) and deep learning, particularly through Self-Supervised Representation Learning (SSRL) for categorical data, has transformed clinical decision-making. This scoping review, followin...
Progressive discretization for generative retrieval: A self-supervised approach to high-quality DocID generation [0.03%]
逐步离散化生成检索的一种自监督的文档 ID 生成方法
Shunyu Yao,Jie Hu,Zhiyuan Zhang et al.
Shunyu Yao et al.
Generative retrieval is a novel retrieval paradigm where large language models serve as differentiable indices to memorize and retrieve candidate documents in a generative fashion. This paradigm overcomes the limitation that documents and q...
Virtual Bonding Enhanced Graph Self-Supervised Learning for Molecular Property Prediction [0.03%]
一种虚拟键合增强的图自监督学习方法用于分子性质预测
Yongna Yuan,Zitian Lu,Yuhan Li
Yongna Yuan
Accurate prediction of molecular properties is essential for modern drug design and discovery. Self-supervised learning (SSL) and Graph Neural Networks (GNNs) have been widely used in this field to learn molecular representations and predic...
Blind single-shot phase retrieval based on a self-supervised physics-adaptive neural network [0.03%]
基于自监督物理适应神经网络的单次盲相位检索方法
Xiaodong Yang,Yixiao Yang,Ziyang Li et al.
Xiaodong Yang et al.
Recently, single-shot phase retrieval techniques, which aim to reconstruct an original sample from a single near-field diffraction pattern, have garnered significant attention. Despite their promise, existing methods are highly dependent on...
SELF-SUPERVISED LEARNING TO IMPROVE TOPOLOGY-OPTIMIZED AXON SEGMENTATION AND CENTERLINE DETECTION [0.03%]
自监督学习在拓扑优化轴突分割和中心线检测中的应用
Nina I Shamsi,Lars A Gjesteby,David Chavez et al.
Nina I Shamsi et al.
Large-scale brain mapping requires preserving the topology of axonal structures to understand how neurons connect throughout the brain and intersect different brain regions. The ability to leverage unannotated data for algorithm development...
Self-supervision enhances instance-based multiple instance learning methods in digital pathology: a benchmark study [0.03%]
自监督增强实例基于多示例学习方法在数字病理中的基准研究
Ali Mammadov,Loïc Le Folgoc,Julien Adam et al.
Ali Mammadov et al.
Purpose: Multiple instance learning (MIL) has emerged as the best solution for whole slide image (WSI) classification. It consists of dividing each slide into patches, which are treated as a bag of instances labeled with ...
Continuous Assessment of Daily-Living Gait Using Self-Supervised Learning of Wrist-Worn Accelerometer Data [0.03%]
基于自我监督学习的腕部加速计数据连续评估日常生活步态
Yonatan E Brand,Aron S Buchman,Felix Kluge et al.
Yonatan E Brand et al.
Physical activity and mobility are critical for healthy aging and predict diverse health outcomes. While wrist-worn accelerometers are widely used to monitor physical activity, estimating gait metrics from wrist data remains challenging. We...
Matrix completion-informed deep unfolded equilibrium models for self-supervised [Formula: see text] -space interpolation in MRI [0.03%]
基于矩阵填充的深度展开平衡模型在MRI中的自监督[公式]|\beta\ |-空间插值方法
Chen Luo,Huayu Wang,Yuanyuan Liu et al.
Chen Luo et al.
Background: Self-supervised methods for magnetic resonance imaging (MRI) reconstruction have garnered significant interest due to their ability to address the challenges of slow data acquisition and scarcity of fully samp...
Self-supervised brain lesion generation for effective data augmentation of medical images [0.03%]
自监督脑病变生成用于医学图像有效数据增强
Jiayu Huo,Sébastien Ourselin,Rachel Sparks
Jiayu Huo
Accurate brain lesion delineation is important for planning neurosurgical treatment. Automatic brain lesion segmentation methods based on convolutional neural networks have demonstrated remarkable performance. However, neural network perfor...
S4R: Separated Self-Supervised Spectral Regression for Hyperspectral Histopathology Image Diagnosis [0.03%]
基于分离的自监督光谱回归的高光谱病理图像诊断方法研究
Yan Wang,Xingran Xie,Lili Gao et al.
Yan Wang et al.
Hyperspectral images (HSIs) offer great potential for computational pathology. But, limited by the lack of adequate annotated data and the high spectral redundancy of HSIs, traditional supervised learning techniques are usually bottle-necke...
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