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Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence

Nature communications, 2024-01, Vol.15 (1), p.742-11, Article 742

2024

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Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas

European radiology, 2020-12, Vol.30 (12), p.6497-6507

2020

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Editorial: Machine learning and deep learning in biomedical signal analysis

Frontiers in human neuroscience, 2023-05, Vol.17, p.1183840-1183840

2023

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Value of digital mammography in predicting lymphovascular invasion of breast cancer

BMC cancer, 2020-04, Vol.20 (1), p.274-274, Article 274

2020

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Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer

Cancer imaging, 2024-10, Vol.24 (1), p.140-13, Article 140

2024

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A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules

Cancer imaging, 2020-07, Vol.20 (1), p.45-45, Article 45

2020

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A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer From Primary Gastric Lymphoma

Frontiers in oncology, 2022-01, Vol.11, p.802205-802205

2022

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Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference

Biomedical engineering online, 2020-06, Vol.19 (1), p.51-51, Article 51

2020

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Phospholipid Phosphatase 4 promotes proliferation and tumorigenesis, and activates Ca2+-permeable Cationic Channel in lung carcinoma cells

Molecular cancer, 2017-08, Vol.16 (1), p.147-147, Article 147

2017

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A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI

BMC medical imaging, 2023-11, Vol.23 (1), p.200-200, Article 200

2023

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Development of a CT image analysis-based scoring system to differentiate gastric schwannomas from gastrointestinal stromal tumors

Frontiers in oncology, 2023-06, Vol.13, p.1057979-1057979

2023

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Comparison of the clinical and MRI features of patients with hepatic hemangioma, epithelioid hemangioendothelioma, or angiosarcoma

BMC medical imaging, 2020-06, Vol.20 (1), p.1-71, Article 71

2020

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Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics

Frontiers in oncology, 2022-09, Vol.12, p.890659-890659

2022

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Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster

IET image processing, 2022-09, Vol.16 (11), p.2947-2958

2022

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Computed Tomography-Based Radiomics Nomogram: Potential to Predict Local Recurrence of Gastric Cancer After Radical Resection

Frontiers in oncology, 2021-09, Vol.11, p.638362-638362

2021

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A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

Frontiers in genetics, 2021-03, Vol.12, p.569318-569318

2021

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Deep learning vs. robust federal learning for distinguishing adrenal metastases from benign lesions with multi-phase CT images

Heliyon, 2024-02, Vol.10 (3), p.e25655-e25655, Article e25655

2024

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Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes

PeerJ (San Francisco, CA), 2020-11, Vol.8, p.e10249-e10249, Article e10249

2020

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Editorial for “Simultaneous multislice readout‐segmented echo planar imaging for diffusion‐weighted MR imaging in patients with invasive breast cancers”

Journal of magnetic resonance imaging, 2021-04, Vol.53 (4), p.1116-1117

2021

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Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast‐enhanced‐MRI‐based radiomics

Journal of magnetic resonance imaging, 2019-09, Vol.50 (3), p.847-857

2019

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Reduced Complexity in Stroke with Motor Deficits: A Resting-State fMRI Study

Neuroscience, 2020-05, Vol.434, p.35-43

2020

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Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks

Physics in medicine & biology, 2020-05, Vol.65 (10), p.105006-105006

2020

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Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule

European journal of radiology, 2020-07, Vol.128, p.109022-109022, Article 109022

2020

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Small lesion classification on abbreviated breast MRI: training can improve diagnostic performance and inter-reader agreement

European radiology, 2022-08, Vol.32 (8), p.5742-5751

2022

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Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes

European radiology, 2023-10, Vol.33 (10), p.6804-6816

2023

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Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence

Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas

Editorial: Machine learning and deep learning in biomedical signal analysis

Value of digital mammography in predicting lymphovascular invasion of breast cancer

Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer

A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules

A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer From Primary Gastric Lymphoma

Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference

Phospholipid Phosphatase 4 promotes proliferation and tumorigenesis, and activates Ca2+-permeable Cationic Channel in lung carcinoma cells

A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI

Development of a CT image analysis-based scoring system to differentiate gastric schwannomas from gastrointestinal stromal tumors

Comparison of the clinical and MRI features of patients with hepatic hemangioma, epithelioid hemangioendothelioma, or angiosarcoma

Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics

Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster

Computed Tomography-Based Radiomics Nomogram: Potential to Predict Local Recurrence of Gastric Cancer After Radical Resection

A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

Deep learning vs. robust federal learning for distinguishing adrenal metastases from benign lesions with multi-phase CT images

Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes

Editorial for “Simultaneous multislice readout‐segmented echo planar imaging for diffusion‐weighted MR imaging in patients with invasive breast cancers”

Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast‐enhanced‐MRI‐based radiomics

Reduced Complexity in Stroke with Motor Deficits: A Resting-State fMRI Study

Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks

Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule

Small lesion classification on abbreviated breast MRI: training can improve diagnostic performance and inter-reader agreement

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes

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