| Machine learning for medical imaging BJ Erickson, P Korfiatis, Z Akkus, TL Kline radiographics 37 (2), 505-515, 2017 | 1809 | 2017 |
| Automated abdominal segmentation of CT scans for body composition analysis using deep learning AD Weston, P Korfiatis, TL Kline, KA Philbrick, P Kostandy, T Sakinis, ... Radiology 290 (3), 669-679, 2019 | 340 | 2019 |
| Toolkits and libraries for deep learning BJ Erickson, P Korfiatis, Z Akkus, T Kline, K Philbrick Journal of digital imaging 30, 400-405, 2017 | 226 | 2017 |
| Residual deep convolutional neural network predicts MGMT methylation status P Korfiatis, TL Kline, DH Lachance, IF Parney, JC Buckner, BJ Erickson Journal of digital imaging 30, 622-628, 2017 | 214 | 2017 |
| MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas P Korfiatis, TL Kline, L Coufalova, DH Lachance, IF Parney, RE Carter, ... Medical physics 43 (6Part1), 2835-2844, 2016 | 175 | 2016 |
| Performance of an artificial multi-observer deep neural network for fully automated segmentation of polycystic kidneys TL Kline, P Korfiatis, ME Edwards, JD Blais, FS Czerwiec, PC Harris, ... Journal of digital imaging 30, 442-448, 2017 | 160 | 2017 |
| Deep learning in radiology: does one size fit all? BJ Erickson, P Korfiatis, TL Kline, Z Akkus, K Philbrick, AD Weston Journal of the American College of Radiology 15 (3), 521-526, 2018 | 145 | 2018 |
| RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning KA Philbrick, AD Weston, Z Akkus, TL Kline, P Korfiatis, T Sakinis, ... Journal of digital imaging 32, 571-581, 2019 | 125 | 2019 |
| Interactive segmentation of medical images through fully convolutional neural networks T Sakinis, F Milletari, H Roth, P Korfiatis, P Kostandy, K Philbrick, Z Akkus, ... arXiv preprint arXiv:1903.08205, 2019 | 110 | 2019 |
| Texture-based identification and characterization of interstitial pneumonia patterns in lung multidetector CT PD Korfiatis, AN Karahaliou, AD Kazantzi, C Kalogeropoulou, ... IEEE transactions on information technology in biomedicine 14 (3), 675-680, 2009 | 108 | 2009 |
| Combining 2D wavelet edge highlighting and 3D thresholding for lung segmentation in thin-slice CT P Korfiatis, S Skiadopoulos, P Sakellaropoulos, C Kalogeropoulou, ... The British journal of radiology 80 (960), 996-1004, 2007 | 95 | 2007 |
| Texture classification‐based segmentation of lung affected by interstitial pneumonia in high‐resolution CT P Korfiatis, C Kalogeropoulou, A Karahaliou, A Kazantzi, S Skiadopoulos, ... Medical physics 35 (12), 5290-5302, 2008 | 88 | 2008 |
| Radiomics-based machine-learning models can detect pancreatic cancer on prediagnostic computed tomography scans at a substantial lead time before clinical diagnosis S Mukherjee, A Patra, H Khasawneh, P Korfiatis, N Rajamohan, G Suman, ... Gastroenterology 163 (5), 1435-1446. e3, 2022 | 85 | 2022 |
| Image texture features predict renal function decline in patients with autosomal dominant polycystic kidney disease TL Kline, P Korfiatis, ME Edwards, KT Bae, A Yu, AB Chapman, M Mrug, ... Kidney international 92 (5), 1206-1216, 2017 | 74 | 2017 |
| What does deep learning see? Insights from a classifier trained to predict contrast enhancement phase from CT images KA Philbrick, K Yoshida, D Inoue, Z Akkus, TL Kline, AD Weston, ... American Journal of Roentgenology 211 (6), 1184-1193, 2018 | 71 | 2018 |
| Automated segmentation of hyperintense regions in FLAIR MRI using deep learning P Korfiatis, TL Kline, BJ Erickson Tomography 2 (4), 334, 2016 | 71 | 2016 |
| Deep learning can see the unseeable: predicting molecular markers from MRI of brain gliomas P Korfiatis, B Erickson Clinical radiology 74 (5), 367-373, 2019 | 65 | 2019 |
| Automatic total kidney volume measurement on follow-up magnetic resonance images to facilitate monitoring of autosomal dominant polycystic kidney disease progression TL Kline, P Korfiatis, ME Edwards, JD Warner, MV Irazabal, BF King, ... Nephrology Dialysis Transplantation 31 (2), 241-248, 2016 | 65 | 2016 |
| Is there a role for B-cell depletion as therapy for scleroderma? A case report and review of the literature D Daoussis, SNC Liossis, AC Tsamandas, C Kalogeropoulou, A Kazantzi, ... Seminars in arthritis and rheumatism 40 (2), 127-136, 2010 | 62 | 2010 |
| Impact of software modeling on the accuracy of perfusion MRI in glioma LS Hu, Z Kelm, P Korfiatis, AC Dueck, C Elrod, BM Ellingson, ... American Journal of Neuroradiology 36 (12), 2242-2249, 2015 | 58 | 2015 |
Bradley J EricksonProfessor of Radiology, Mayo ClinicVerified email at mayo.edu
