| Machine learning for medical imaging BJ Erickson, P Korfiatis, Z Akkus, TL Kline radiographics 37 (2), 505-515, 2017 | 1830 | 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 | 353 | 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 | 227 | 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 | 210 | 2017 |
| Algorithm-enabled low-dose micro-CT imaging X Han, J Bian, DR Eaker, TL Kline, EY Sidky, EL Ritman, X Pan IEEE transactions on medical imaging 30 (3), 606-620, 2010 | 183 | 2010 |
| 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 | 180 | 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 | 162 | 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 | 147 | 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 | 123 | 2019 |
| Monoallelic IFT140 pathogenic variants are an important cause of the autosomal dominant polycystic kidney-spectrum phenotype SR Senum, YSM Li, KA Benson, G Joli, E Olinger, S Lavu, CD Madsen, ... The American Journal of Human Genetics 109 (1), 136-156, 2022 | 113 | 2022 |
| Biocompatible high-moment FeCo-Au magnetic nanoparticles for magnetic hyperthermia treatment optimization TL Kline, YH Xu, Y Jing, JP Wang Journal of Magnetism and Magnetic Materials 321 (10), 1525-1528, 2009 | 107 | 2009 |
| Long-term administration of tolvaptan in autosomal dominant polycystic kidney disease ME Edwards, FT Chebib, MV Irazabal, TG Ofstie, LA Bungum, AJ Metzger, ... Clinical Journal of the American Society of Nephrology 13 (8), 1153-1161, 2018 | 105 | 2018 |
| 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 | 92 | 2022 |
| Automatic measurement of kidney and liver volumes from MR images of patients affected by autosomal dominant polycystic kidney disease MDA van Gastel, ME Edwards, VE Torres, BJ Erickson, RT Gansevoort, ... Journal of the American Society of Nephrology 30 (8), 1514-1522, 2019 | 92 | 2019 |
| Randomised controlled trial to determine the efficacy and safety of prescribed water intake to prevent kidney failure due to autosomal dominant polycystic kidney disease … ATY Wong, C Mannix, JJ Grantham, M Allman-Farinelli, SV Badve, ... BMJ open 8 (1), e018794, 2018 | 88 | 2018 |
| Noninvasive assessment of renal fibrosis with magnetization transfer MR imaging: validation and evaluation in murine renal artery stenosis K Jiang, CM Ferguson, B Ebrahimi, H Tang, TL Kline, TA Burningham, ... Radiology 283 (1), 77-86, 2017 | 87 | 2017 |
| 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 |
| The value of genotypic and imaging information to predict functional and structural outcomes in ADPKD S Lavu, LE Vaughan, SR Senum, TL Kline, AB Chapman, RD Perrone, ... JCI insight 5 (15), e138724, 2020 | 72 | 2020 |
| 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 | 72 | 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 |
Bradley J EricksonProfessor of Radiology, Mayo ClinicVerified email at mayo.edu
