| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 2291 | 2018 |
| High-throughput discovery of novel developmental phenotypes ME Dickinson, AM Flenniken, X Ji, L Teboul, MD Wong, JK White, ... Nature 537 (7621), 508-514, 2016 | 1162 | 2016 |
| Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks JM Brown, JP Campbell, A Beers, K Chang, S Ostmo, RVP Chan, J Dy, ... JAMA ophthalmology 136 (7), 803-810, 2018 | 632 | 2018 |
| Distributed deep learning networks among institutions for medical imaging K Chang, N Balachandar, C Lam, D Yi, J Brown, A Beers, B Rosen, ... Journal of the American Medical Informatics Association 25 (8), 945-954, 2018 | 375 | 2018 |
| Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium TF Meehan, N Conte, DB West, JO Jacobsen, J Mason, J Warren, ... Nature genetics 49 (8), 1231-1238, 2017 | 243 | 2017 |
| Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement K Chang, AL Beers, HX Bai, JM Brown, KI Ly, X Li, JT Senders, ... Neuro-oncology 21 (11), 1412-1422, 2019 | 190 | 2019 |
| Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity TK Redd, JP Campbell, JM Brown, SJ Kim, S Ostmo, RVP Chan, J Dy, ... British Journal of Ophthalmology 103 (5), 580-584, 2019 | 181 | 2019 |
| ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI S Winzeck, A Hakim, R McKinley, JA Pinto, V Alves, C Silva, M Pisov, ... Frontiers in neurology, 679, 2018 | 178 | 2018 |
| High-resolution medical image synthesis using progressively grown generative adversarial networks A Beers, J Brown, K Chang, JP Campbell, S Ostmo, MF Chiang, ... arXiv preprint arXiv:1805.03144, 2018 | 140 | 2018 |
| Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging MD Li, K Chang, B Bearce, CY Chang, AJ Huang, JP Campbell, ... NPJ digital medicine 3 (1), 48, 2020 | 121 | 2020 |
| Monitoring disease progression with a quantitative severity scale for retinopathy of prematurity using deep learning S Taylor, JM Brown, K Gupta, JP Campbell, S Ostmo, RVP Chan, J Dy, ... JAMA ophthalmology 137 (9), 1022-1028, 2019 | 115 | 2019 |
| Applications of artificial intelligence for retinopathy of prematurity screening JP Campbell, P Singh, TK Redd, JM Brown, PK Shah, P Subramanian, ... Pediatrics 147 (3), 2021 | 85 | 2021 |
| A quantitative severity scale for retinopathy of prematurity using deep learning to monitor disease regression after treatment K Gupta, JP Campbell, S Taylor, JM Brown, S Ostmo, RVP Chan, J Dy, ... JAMA ophthalmology 137 (9), 1029-1036, 2019 | 80 | 2019 |
| Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation A Beers, K Chang, J Brown, E Sartor, CP Mammen, E Gerstner, B Rosen, ... arXiv preprint arXiv:1709.02967, 2017 | 78 | 2017 |
| Automated fundus image quality assessment in retinopathy of prematurity using deep convolutional neural networks AS Coyner, R Swan, JP Campbell, S Ostmo, JM Brown, ... Ophthalmology Retina 3 (5), 444-450, 2019 | 69 | 2019 |
| Plus disease in retinopathy of prematurity: convolutional neural network performance using a combined neural network and feature extraction approach VM Yildiz, P Tian, I Yildiz, JM Brown, J Kalpathy-Cramer, J Dy, S Ioannidis, ... Translational Vision Science & Technology 9 (2), 10-10, 2020 | 60 | 2020 |
| Evaluation of a Deep Learning–Derived Quantitative Retinopathy of Prematurity Severity Scale JP Campbell, SJ Kim, JM Brown, S Ostmo, RVP Chan, J Kalpathy-Cramer, ... Ophthalmology 128 (7), 1070-1076, 2021 | 58 | 2021 |
| DeepNeuro: an open-source deep learning toolbox for neuroimaging A Beers, J Brown, K Chang, K Hoebel, J Patel, KI Ly, SM Tolaney, ... Neuroinformatics, 1-14, 2020 | 55 | 2020 |
| Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture MA Silva, J Patel, V Kavouridis, T Gallerani, A Beers, K Chang, KV Hoebel, ... World neurosurgery 131, e46-e51, 2019 | 55 | 2019 |
| Radiomics repeatability pitfalls in a scan-rescan MRI study of glioblastoma KV Hoebel, JB Patel, AL Beers, K Chang, P Singh, JM Brown, MC Pinho, ... Radiology: Artificial Intelligence 3 (1), e190199, 2020 | 50 | 2020 |
Jayashree Kalpathy-CramerUniversity of ColoradoEmail được xác minh tại cuanschutz.edu
