Sandy Napel
Affiliations: | Stanford University, Palo Alto, CA |
Area:
Computer Science, Biomedical Engineering, Radiology, Medical BiophysicsGoogle:
"Sandy Napel"Children
Sign in to add trainee| David S. Paik | grad student | 2002 | Stanford |
| Shaohua Sun | grad student | 2007 | Stanford |
| Padmavathi Sundaram | grad student | 2007 | Stanford |
| Feng Zhuge | grad student | 2007 | Stanford |
| Tejas S. Rakshe | grad student | 2008 | Stanford |
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Publications
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| Mattonen SA, Gude D, Echegaray S, et al. (2020) Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines. Journal of Medical Imaging (Bellingham, Wash.). 7: 042803 |
| Zwanenburg A, Vallières M, Abdalah MA, et al. (2020) The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 191145 |
| Mukherjee P, Zhou M, Lee E, et al. (2020) A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets Nature Machine Intelligence. 2: 274-282 |
| Mattonen SA, Davidzon GA, Benson J, et al. (2019) Bone Marrow and Tumor Radiomics at F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer. Radiology. 190357 |
| Tunali I, Hall LO, Napel S, et al. (2019) Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions. Medical Physics |
| Napel S, Mu W, Jardim-Perassi BV, et al. (2018) Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats. Cancer |
| Bakr S, Gevaert O, Echegaray S, et al. (2018) A radiogenomic dataset of non-small cell lung cancer. Scientific Data. 5: 180202 |
| Bernard O, Lalande A, Zotti C, et al. (2018) Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved? Ieee Transactions On Medical Imaging |
| Wu J, Cao G, Sun X, et al. (2018) Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy. Radiology. 172462 |
| Balagurunathan Y, Beers A, Kalpathy-Cramer J, et al. (2018) Semi-Automated Pulmonary Nodule Interval Segmentation using the NLST data. Medical Physics |