28 results on '"Chen, Yehang"'
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2. A novel design for refractory complex concentrated alloys based on multi-objective bi-level optimization
3. A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI
4. Distinguishing common renal cell carcinomas from benign renal tumors based on machine learning: comparing various CT imaging phases, slices, tumor sizes, and ROI segmentation strategies
5. MIP image derived from abbreviated breast MRI: potential to reduce unnecessary sub-nipple biopsies during nipple-sparing mastectomy for breast cancer
6. Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence
7. Federated Learning based on Feature Transfer: Multi-center Nuclear Segmentation of Histological Images
8. Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas
9. A Heterogeneity Radiomic Nomogram for Preoperative Differentiation of Primary Gastric Lymphoma From Borrmann Type IV Gastric Cancer
10. A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules
11. Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference
12. Whole-Lesion Computed Tomography–Based Entropy Parameters for the Differentiation of Minimally Invasive and Invasive Adenocarcinomas Appearing as Pulmonary Subsolid Nodules
13. Identifying Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma: Exploring Robust Image Features with Cross-Domain Transfer Learning
14. 基于自适应聚合权重联邦学习的肺结节CT图像分类
15. A brain-like classification method for computed tomography images based on adaptive feature matching dual-source domain heterogeneous transfer learning
16. Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics
17. Active contour model of breast cancer DCE‐MRI segmentation with an extreme learning machine and a fuzzy C‐means cluster
18. sj-docx-1-acr-10.1177_02841851211058934 - Supplemental material for Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer
19. A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer From Primary Gastric Lymphoma
20. Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer
21. A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma
22. Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer.
23. Computed Tomography-Based Radiomics Nomogram: Potential to Predict Local Recurrence of Gastric Cancer After Radical Resection
24. MIP image derived from abbreviated breast MRI: potential to reduce unnecessary sub-nipple biopsies during nipple-sparing mastectomy for breast cancer
25. Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule
26. A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography
27. Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics
28. A Heterogeneity Radiomic Nomogram for Preoperative Differentiation of Primary Gastric Lymphoma From Borrmann Type IV Gastric Cancer.
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