Napel, Sandy

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020) · Radiology

Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification Techniques

Cited by 4,177

Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?

Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P.-A., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M. A., et al. (2018) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesCardiac Imaging and Diagnostics

Cited by 2,431

The Medical Segmentation Decathlon

Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al. (2022) · Nature Communications

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 1,384

FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., et al. (2025) · BMJ

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 957

Content-based image retrieval in radiology: current status and future directions

Akgül, C. B., Rubin, D. L., Napel, S., Beaulieu, C. F., Greenspan, H., & Acar, B. (2011) · Journal of Digital Imaging

Image Retrieval and Classification TechniquesRadiomics and Machine Learning in Medical Imaging

Cited by 388

Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features

Gevaert, O., Mitchell, L. A., Achrol, A. S., Xu, J., Echegaray, S., Steinberg, G. K., Cheshier, S. H., Napel, S., Zaharchuk, G., & Plevritis, S. K. (2014) · Radiology

Radiomics and Machine Learning in Medical ImagingGlioma Diagnosis and Treatment

Cited by 319

A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound

Lekadir, K., Galimzianova, A., Betriu, A., del Mar Vila, M., Igual, L., Rubin, D. L., Fernandez, E., Radeva, P., & Napel, S. (2017) · IEEE Journal of Biomedical and Health Informatics

Cerebrovascular and Carotid Artery DiseasesMedical Image Segmentation Techniques

Cited by 212

Predictive radiogenomics modeling of EGFR mutation status in lung cancer

Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Jensen, K. C., Berry, G. J., Guo, H. H., Lau, C., Plevritis, S. K., et al. (2017) · Scientific Reports

Radiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and Treatment

Cited by 160

Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer

Wu, J., Li, X., Teng, X., Rubin, D. L., Napel, S., Daniel, B. L., & Li, R. (2018) · Breast Cancer Research

MRI in cancer diagnosisRadiomics and Machine Learning in Medical Imaging

Cited by 53

Artificial Intelligence in Radiology: Opportunities and Challenges

Flory, M. N., Napel, S., & Tsai, E. B. (2024) · Seminars in Ultrasound, CT and MRI

Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Cited by 29