| Identifying medical diagnoses and treatable diseases by image-based deep learning DS Kermany, M Goldbaum, W Cai, CCS Valentim, H Liang, SL Baxter, ... cell 172 (5), 1122-1131. e9, 2018 | 4217 | 2018 |
| Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response AD Hoover, V Kouznetsova, M Goldbaum IEEE Transactions on Medical imaging 19 (3), 203-210, 2000 | 3064 | 2000 |
| Detection of blood vessels in retinal images using two-dimensional matched filters S Chaudhuri, S Chatterjee, N Katz, M Nelson, M Goldbaum IEEE Transactions on medical imaging 8 (3), 263-269, 1989 | 2345 | 1989 |
| Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels A Hoover, M Goldbaum IEEE transactions on medical imaging 22 (8), 951-958, 2003 | 1055 | 2003 |
| Labeled optical coherence tomography (oct) and chest x-ray images for classification D Kermany, K Zhang, M Goldbaum Mendeley data 2 (2), 651, 2018 | 783 | 2018 |
| Beneficial effects of intensive therapy of diabetes during adolescence: outcomes after the conclusion of the Diabetes Control and Complications Trial (DCCT) PA Cleary, W Dahms, D Goldstein, J Malone, WV Tamborlane J Pediatr 139 (1), 804-12, 2001 | 615 | 2001 |
| Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence H Liang, BY Tsui, H Ni, CCS Valentim, SL Baxter, G Liu, W Cai, ... Nature medicine 25 (3), 433-438, 2019 | 602 | 2019 |
| Sustained effect of intensive treatment of type 1 diabetes mellitus on development and progression of diabetic nephropathy: the Epidemiology of Diabetes Interventions and … TWT for the Diabetes, ... JAMA: the journal of the American Medical Association 290 (16), 2159, 2003 | 587 | 2003 |
| Measurement and classification of retinal vascular tortuosity WE Hart, M Goldbaum, B Côté, P Kube, MR Nelson International journal of medical informatics 53 (2-3), 239-252, 1999 | 387 | 1999 |
| Performance of deep learning architectures and transfer learning for detecting glaucomatous optic neuropathy in fundus photographs M Christopher, A Belghith, C Bowd, JA Proudfoot, MH Goldbaum, ... Scientific reports 8 (1), 16685, 2018 | 308 | 2018 |
| Comparison of machine learning and traditional classifiers in glaucoma diagnosis K Chan, TW Lee, PA Sample, MH Goldbaum, RN Weinreb, TJ Sejnowski IEEE Transactions on Biomedical Engineering 49 (9), 963-974, 2002 | 266 | 2002 |
| Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images M Goldbaum, S Moezzi, A Taylor, S Chatterjee, J Boyd, E Hunter, R Jain Proceedings of 3rd IEEE international conference on image processing 3, 695-698, 1996 | 228 | 1996 |
| Large dataset of labeled optical coherence tomography (oct) and chest x-ray images D Kermany, K Zhang, M Goldbaum Mendeley Data 3 (10.17632), 2018 | 181 | 2018 |
| Silicone oil tamponade to seal macular holes without position restrictions MH Goldbaum, BW McCuen 2nd, AM Hanneken, SK Burgess, HH Chen Ophthalmology 105 (11), 2140-2148, 1998 | 159 | 1998 |
| Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry MH Goldbaum, PA Sample, K Chan, J Williams, TW Lee, E Blumenthal, ... Investigative ophthalmology & visual science 43 (1), 162-169, 2002 | 152 | 2002 |
| Comparing neural networks and linear discriminant functions for glaucoma detection using confocal scanning laser ophthalmoscopy of the optic disc C Bowd, K Chan, LM Zangwill, MH Goldbaum, TW Lee, TJ Sejnowski, ... Investigative ophthalmology & visual science 43 (11), 3444-3454, 2002 | 149 | 2002 |
| Deep learning approaches predict glaucomatous visual field damage from OCT optic nerve head en face images and retinal nerve fiber layer thickness maps M Christopher, C Bowd, A Belghith, MH Goldbaum, RN Weinreb, ... Ophthalmology 127 (3), 346-356, 2020 | 138 | 2020 |
| Interpretation of automated perimetry for glaucoma by neural network. MH Goldbaum, PA Sample, H White, B Colt, P Raphaelian, RD Fechtner, ... Investigative ophthalmology & visual science 35 (9), 3362-3373, 1994 | 134 | 1994 |
| Retinal nerve fiber layer features identified by unsupervised machine learning on optical coherence tomography scans predict glaucoma progression M Christopher, A Belghith, RN Weinreb, C Bowd, MH Goldbaum, ... Investigative ophthalmology & visual science 59 (7), 2748-2756, 2018 | 133 | 2018 |
| Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers LM Zangwill, K Chan, C Bowd, J Hao, TW Lee, RN Weinreb, TJ Sejnowski, ... Investigative ophthalmology & visual science 45 (9), 3144-3151, 2004 | 130 | 2004 |
Kang ZhangMacau University of Science and TechnologyVerified email at must.edu.mo
