Yingli Sun

发表

Low-dose CT examination for rib fracture evaluation

F. Gao, Liang Jin, Xiao-jun Ge, 2018, Medicine.

3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas.

Bingbing Ni, Peijun Wang, Jiancheng Yang, 2018, Cancer research.

Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction

Shao-Feng Duan, Wei Zhao, Liang Jin, 2020, European Radiology.

Quantitative evaluation of postintervention foot blood supply in patients with peripheral artery disease by computed tomography perfusion.

Wei Zhao, Liang Jin, Yingli Sun, 2020, Journal of vascular surgery.

Qualitative and quantitative image analysis of 16 cm wide-coverage computed tomography compared to new-generation dual-source CT.

Liang Jin, Yingli Sun, Ming Li, 2020, Journal of X-ray science and technology.

Use of an Anthropomorphic Chest Model to Evaluate Multiple Scanning Protocols for High-Definition and Standard-Definition Computed Tomography to Detect Small Pulmonary Nodules

Liang Jin, Yingli Sun, Ming Li, 2019, Medical science monitor : international medical journal of experimental and clinical research.

The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma

Jun Liu, Y. Hua, Wei Zhao, 2020, Frontiers in Oncology.

Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes

Peijun Wang, Jiancheng Yang, Y. Hua, 2019, Thoracic cancer.

ViSTA: A Novel Network Improving Lung Adenocarcinoma Invasiveness Prediction from Follow-Up CT Series

Bingbing Ni, J. Liu, Jiancheng Yang, 2022, Cancers.

A radiomics nomogram for invasiveness prediction in lung adenocarcinoma manifesting as part-solid nodules with solid components smaller than 6 mm

Yingli Sun, Quan Zhu, Guang Yang, 2022, Frontiers in Oncology.

Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinoma

J. Liu, Wei Zhao, Yingli Sun, 2022, Frontiers in Medicine.

Diagnosis of Acute Aortic Syndromes on Non-Contrast CT Images with Radiomics-Based Machine Learning

Ming Li, Yingli Sun, P. Gao, 2023, Biology.

Bodyweight-adjusted Contrast Media With Shortened Injection Duration for Step-and-Shoot Coronary Computed Tomography Angiography to Acquire Improved Image Quality

Yingli Sun, Cheng Li, P. Gao, 2023, Journal of thoracic imaging.

Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics

Yue Wu, Yingli Sun, P. Gao, 2021, Frontiers in Oncology.

Development and validation of a radiomics nomogram for identifying invasiveness of pulmonary adenocarcinomas appearing as subcentimeter ground-glass opacity nodules.

Peijun Wang, Y. Hua, Wei Zhao, 2019, European journal of radiology.

Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet

Bingbing Ni, Jiajun Chen, Liang Jin, 2020, EBioMedicine.

CT characterization of different pathological types of subcentimeter pulmonary ground-glass nodular lesions.

Y. Hua, Yingli Sun, Ming Li, 2019, The British journal of radiology.

Three-Dimensional Convolutional Neural Network-Based Prediction of Epidermal Growth Factor Receptor Expression Status in Patients With Non-Small Cell Lung Cancer

Ming Li, Yingli Sun, P. Gao, 2022, Frontiers in Oncology.

Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning

Bingbing Ni, Wei Zhao, Peijun Wang, 2019, Cancer medicine.

Contrast medium administration with a body surface area protocol in step-and-shoot coronary computed tomography angiography with dual-source scanners

Wei Zhao, Yingli Sun, Cheng Li, 2020, Scientific Reports.

GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status.

Bingbing Ni, J. Liu, Jiancheng Yang, 2022, IEEE transactions on neural networks and learning systems.

High-resolution CT image analysis based on 3D convolutional neural network can enhance the classification performance of radiologists in classifying pulmonary non-solid nodules.

Tong-fu Yu, Yingli Sun, Yida Wang, 2021, European journal of radiology.

Computed tomography radiomics in growth prediction of pulmonary ground-glass nodules.

Wei Zhao, Yingli Sun, P. Gao, 2022, European journal of radiology.

3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas.

Bingbing Ni, Peijun Wang, Jiancheng Yang, 2018, Cancer research.

Diagnostic value of contrast-enhanced CT scans in identifying lung adenocarcinomas manifesting as GGNs (ground glass nodules)

Y. Hua, Yingli Sun, Ming Li, 2017, Medicine.