Bin Zheng, Ph.D.
Affiliations: | 1997-2002 | University of California, San Diego, La Jolla, CA | |
| 2003-2010 | Harvard Medical School | ||
| 2010-2013 | Columbia University, New York, NY | ||
| 2013-2023 | Harvard Medical School, Boston, MA, United States | ||
| 2023- | Cedars-Sinai Medical Center, Los Angeles, CA, United States |
Area:
Cell BiologyGoogle:
"Bin Zheng"Mean distance: 24.01 (cluster 31)
Parents
Sign in to add mentor| Marilyn Farquhar | grad student | 2002 | UCSD | |
| (RGS proteins: Bridging the 'GAP's between G protein signaling and membrane trafficking.) | ||||
| Lewis C. Cantley | post-doc | 2003-2010 | (Chemistry Tree) | |
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Publications
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| Zheng B, Qiu Y, Aghaei F, et al. (2019) Developing global image feature analysis models to predict cancer risk and prognosis. Visual Computing For Industry, Biomedicine and Art. 2: 17 |
| Gong J, Liu J, Hao W, et al. (2019) A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. European Radiology |
| Heidari M, Mirniaharikandehei S, Liu W, et al. (2019) Development and Assessment of a New Global Mammographic Image Feature Analysis Scheme to Predict Likelihood of Malignant Cases. Ieee Transactions On Medical Imaging |
| Chen X, Zargari A, Hollingsworth AB, et al. (2019) Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer. Computer Methods and Programs in Biomedicine. 179: 104995 |
| Mirniaharikandehei S, VanOsdol J, Heidari M, et al. (2019) Developing a Quantitative Ultrasound Image Feature Analysis Scheme to Assess Tumor Treatment Efficacy Using a Mouse Model. Scientific Reports. 9: 7293 |
| Essel K, Qiu Y, Thai T, et al. (2019) Quantitative computed tomography image feature analysis predicts response to immune checkpoint inhibitors in gynecologic cancers Gynecologic Oncology. 154: 172-173 |
| Dai Y, Yan S, Zheng B, et al. (2018) Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification. Physics in Medicine and Biology |
| Gong J, Liu JY, Jiang YJ, et al. (2018) Fusion of Quantitative Imaging Features and Serum Biomarkers to Improve Performance of Computer-aided Diagnosis Scheme for Lung Cancer: A Preliminary Study. Medical Physics |
| Gao F, Wu T, Li J, et al. (2018) SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis. Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society. 70: 53-62 |
| Du Y, Zhang R, Zargari A, et al. (2018) Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks. Annals of Biomedical Engineering |