Ruize Yu

Multimodal brain age prediction reveals dissociable signatures of health, cognition and disease risk in 24,648 UK Biobank participants

Ruize Yu, Shanshan Shao, Feng Xu

Abstract Deep-learning brain-age models usually rely on a single MRI contrast, although brain aging affects grey matter, white matter, iron-rich nuclei and cerebrovascular tissue. Here we trained 3D DenseNet121 models to predict chronological age from five MRI modalities - T1, T2 FLAIR, …

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Association Between Interpersonal Relationships and Academic Achievement: A Multilevel Meta-Analysis

Yuting Ding, Tong Zhou, X.C. Li, Ruize Yu et autres

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Gaslighting in parent-child relationships: Psychometric validation and longitudinal associations with depressive symptoms in Chinese samples

Xi Li, Ruize Yu, Yuting Ding, Qin He et autres

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Radiomics and deep learning methods for predicting the growth of subsolid nodules based on CT images

Wanying Yan, Yiqiu Shi, Xinyu Pan, Ruize Yu et autres

The growth of subsolid nodules (SSNs) is a strong predictor of lung adenocarcinoma. However, the heterogeneity in the biological behavior of SSNs poses significant challenges for clinical management. This study aimed to evaluate the clinical utility of deep learning and radiomics approaches …

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End-to-end deep learning for the diagnosis of pelvic and sacral tumors using non-enhanced MRI: a multi-center study

Ping Yin, Ke Liu, Runrong Chen, Yang Liu et autres

This study developed an end-to-end deep learning (DL) model using non-enhanced MRI to diagnose benign and malignant pelvic and sacral tumors (PSTs). Retrospective data from 835 patients across four hospitals were employed to train, validate, and test the models. Six diagnostic models …

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Breaking the Bottleneck: User-Specific Optimization and Real-Time Inference Integration for Sequential Recommendation

W. Xie, Hao Wang, Minghao Fang, Ruize Yu et autres

Sequential recommendation (SR), as an important branch of recommendation systems, has garnered significant attention due to its substantial commercial value. This has inspired some researchers to draw from the successful experiences of large language models to develop scaling laws for SR. However, …

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Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study

Ping Yin, Weidao Chen, Qianrui Fan, Ruize Yu et autres

BACKGROUND: Accurate segmentation of pelvic and sacral tumors (PSTs) in multi-sequence magnetic resonance imaging (MRI) is essential for effective treatment and surgical planning. PURPOSE: To develop a deep learning (DL) framework for efficient segmentation of PSTs from multi-sequence MRI. MATERIALS AND METHODS: …

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Epithelial stem cells from human small bronchi offer a potential for therapy of idiopathic pulmonary fibrosis

Zeyu Liu, Qi Zheng, Zhoubin Li, Moli Huang et autres

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a fibrosing interstitial pneumonia with restrictive ventilation. Recently, the structural and functional defects of small airways have received attention in the early pathogenesis of IPF. This study aimed to elucidate the characteristics of small airway epithelial …

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Identification of Bipolar Disorder and Schizophrenia Based on Brain CT and Deep Learning Methods

Meilin Li, Xingyu Hou, Wanying Yan, Ruize Yu et autres

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HE-Mind: A model for automatically predicting hematoma expansion after spontaneous intracerebral hemorrhage

Zhiming Zhou, Weidao Chen, Ruize Yu, Yuanyuan Chen et autres

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A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study

Xue Liang, Xinyu Li, Guosheng Li, Bing Hui Wang et autres

BACKGROUND: Recently, patients with type 2 diabetes mellitus (T2DM) have experienced a higher incidence and severer degree of vascular calcification (VC), which leads to an increase in the incidence and mortality of vascular complications in patients with T2DM. HYPOTHESIS: To construct and …

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Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study

Yuting Li, Ruize Yu, Huan J. Chang, Wanying Yan et autres

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