| Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms T Schaffter, DSM Buist, CI Lee, Y Nikulin, D Ribli, Y Guan, W Lotter, Z Jie, ... JAMA network open 3 (3), e200265-e200265, 2020 | 316 | 2020 |
| Unsupervised Deformable Registration for Multi-modal Images via Disentangled Representations C Qin, B Shi, R Liao, T Mansi, D Rueckert, A Kamen International Conference on Information Processing in Medical Imaging, 249-261, 2019 | 136 | 2019 |
| Nonlinear feature transformation and deep fusion for Alzheimer's Disease staging analysis B Shi, Y Chen, P Zhang, CD Smith, J Liu, ... Pattern recognition 63, 487-498, 2017 | 104 | 2017 |
| Anomaly detection for medical images based on a one-class classification Q Wei, Y Ren, R Hou, B Shi, JY Lo, L Carin Medical Imaging 2018: Computer-Aided Diagnosis 10575, 105751M, 2018 | 97 | 2018 |
| Prediction of occult invasive disease in ductal carcinoma in situ using deep learning features B Shi, LJ Grimm, MA Mazurowski, JA Baker, JR Marks, LM King, ... Journal of the American College of Radiology 15 (3), 527-534, 2018 | 76 | 2018 |
| Hippocampus segmentation through multi-view ensemble ConvNets Y Chen, B Shi, Z Wang, P Zhang, CD Smith, J Liu Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on …, 2017 | 63 | 2017 |
| Autonomous Detection and Classification of PI-RADS Lesions in an MRI Screening Population Incorporating Multicenter-labeled Deep Learning and Biparametric Imaging: Proof of Concept DJ Winkel, C Wetterauer, MO Matthias, B Lou, B Shi, A Kamen, ... Diagnostics 10 (11), 951, 2020 | 41 | 2020 |
| False Positive Reduction Using Multiscale Contextual Features for Prostate Cancer Detection in Multi-Parametric MRI Scans X Yu, B Lou, B Shi, D Winkel, N Arrahmane, M Diallo, T Meng, ... 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 1355-1359, 2020 | 38 | 2020 |
| Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble Y Chen, B Shi, Z Wang, T Sun, CD Smith, J Liu International Workshop on Machine Learning in Medical Imaging, 88-96, 2017 | 30 | 2017 |
| Nonlinear metric learning for kNN and SVMs through geometric transformations B Shi, J Liu Neurocomputing 318, 18-29, 2018 | 22 | 2018 |
| Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features? B Shi, LJ Grimm, MA Mazurowski, JA Baker, JR Marks, LM King, ... Academic radiology 24 (9), 1139-1147, 2017 | 22 | 2017 |
| Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning … DJ Winkel, HC Breit, B Shi, DT Boll, HH Seifert, C Wetterauer Quantitative Imaging in Medicine and Surgery 10 (4), 808, 2020 | 20 | 2020 |
| Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open. 2020; 3 (3): 200265 T Schaffter, DSM Buist, CI Lee, Y Nikulin, D Ribli, Y Guan, W Lotter, Z Jie, ... | 20 | 2020 |
| Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning B Shi, R Hou, MA Mazurowski, LJ Grimm, Y Ren, JR Marks, LM King, ... Medical Imaging 2018: Computer-Aided Diagnosis 10575, 105752R, 2018 | 17 | 2018 |
| LOCALIZATION AND CLASSIFICATION OF ABNORMALITIES IN MEDICAL IMAGES A Kamen, A Tuysuzoglu, B Lou, B Shi, N Von Roden, K Abdelrahman, ... US Patent App. 15/733,778, 2021 | 14 | 2021 |
| Quad-mesh based radial distance biomarkers for alzheimer's disease KH Hobbs, P Zhang, B Shi, CD Smith, J Liu Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, 19-23, 2016 | 12 | 2016 |
| Distance-informed metric learning for Alzheimer's disease staging B Shi, Z Wang, J Liu Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual …, 2014 | 12 | 2014 |
| Unsupervised deformable registration for multi-modal images B Shi, C Qin, R Liao, T Mansi, A Kamen US Patent 11,158,069, 2021 | 9 | 2021 |
| Nonlinear Metric Learning for Alzheimer's Disease Diagnosis with Integration of Longitudinal Neuroimaging Features. B Shi, Y Chen, K Hobbs, CD Smith, J Liu BMVC, 138.1-138.13, 2015 | 9 | 2015 |
| A combined local and global motion estimation and compensation method for cardiac CT Q Tang, B Chiang, A Akinyemi, A Zamyatin, B Shi, S Nakanishi Medical Imaging 2014: Physics of Medical Imaging 9033, 903304, 2014 | 9 | 2014 |
