| Digital mammographic tumor classification using transfer learning from deep convolutional neural networks BQ Huynh, H Li, ML Giger Journal of Medical Imaging 3 (3), 034501-034501, 2016 | 610 | 2016 |
| A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets N Antropova*, BQ Huynh*, ML Giger Medical physics 44 (10), 5162-5171, 2017 | 399 | 2017 |
| Frequency of routine testing for coronavirus disease 2019 (COVID-19) in high-risk healthcare environments to reduce outbreaks ET Chin*, BQ Huynh*, LAC Chapman, M Murrill, S Basu, NC Lo Clinical Infectious Diseases 73 (9), e3127-e3129, 2020 | 124* | 2020 |
| Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms H Li, ML Giger, BQ Huynh, NO Antropova Journal of medical imaging 4 (4), 041304-041304, 2017 | 104 | 2017 |
| MO‐DE‐207B‐06: Computer‐aided diagnosis of breast ultrasound images using transfer learning from deep convolutional neural networks B Huynh, K Drukker, M Giger Medical physics 43 (6Part30), 3705-3705, 2016 | 85 | 2016 |
| Routine asymptomatic testing strategies for airline travel during the COVID-19 pandemic: a simulation study MV Kiang, ET Chin, BQ Huynh, LAC Chapman, I Rodríguez-Barraquer, ... The Lancet Infectious Diseases 21 (7), 929-938, 2021 | 67 | 2021 |
| Public health impacts of an imminent Red Sea oil spill BQ Huynh, LH Kwong, MV Kiang, ET Chin, AM Mohareb, AO Jumaan, ... Nature sustainability 4 (12), 1084-1091, 2021 | 46 | 2021 |
| Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks N Antropova, B Huynh, H Li, ML Giger Journal of Medical Imaging 6 (1), 011002-011002, 2019 | 43 | 2019 |
| Comparison of breast DCE-MRI contrast time points for predicting response to neoadjuvant chemotherapy using deep convolutional neural network features with transfer learning BQ Huynh, N Antropova, ML Giger Medical imaging 2017: computer-aided diagnosis 10134, 207-213, 2017 | 42 | 2017 |
| Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study ET Chin*, BQ Huynh*, NC Lo, T Hastie, S Basu BMC medicine 18 (1), 1-8, 2020 | 41* | 2020 |
| SU‐D‐207B‐06: Predicting breast cancer malignancy on DCE‐MRI data using pre‐trained convolutional neural networks N Antropova, B Huynh, M Giger Medical physics 43 (6Part4), 3349-3350, 2016 | 40 | 2016 |
| Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen BQ Huynh, S Basu Disaster Medicine and Public Health Preparedness, 2019 | 24 | 2019 |
| Performance comparison of deep learning and segmentation-based radiomic methods in the task of distinguishing benign and malignant breast lesions on DCE-MRI N Antropova, B Huynh, M Giger Medical imaging 2017: Computer-aided diagnosis 10134, 369-373, 2017 | 17 | 2017 |
| Recurrent neural networks for breast lesion classification based on DCE-MRIs N Antropova, B Huynh, M Giger Medical imaging 2018: Computer-aided diagnosis 10575, 593-598, 2018 | 16 | 2018 |
| Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle-and high-income countries: the International Surgical Outcomes Study group T Ahmad, RA Bouwman, I Grigoras, C Aldecoa, C Hofer, A Hoeft, P Holt, ... British journal of anaesthesia 117 (5), 601-+, 2016 | 14 | 2016 |
| No evidence of inflated mortality reporting from the Gaza Ministry of Health BQ Huynh, ET Chin, PB Spiegel The Lancet 403 (10421), 23-24, 2024 | 8 | 2024 |
| Deep learning and three-compartment breast imaging in breast cancer diagnosis K Drukker, BQ Huynh, ML Giger, S Malkov, JI Avila, B Fan, B Joe, ... Medical Imaging 2017: Computer-Aided Diagnosis 10134, 363-368, 2017 | 7 | 2017 |
| Potential for allocative harm in an environmental justice data tool BQ Huynh*, ET Chin*, A Koenecke, D Ouyang, DE Ho, MV Kiang, ... arXiv preprint arXiv:2304.05603, 2023 | 2 | 2023 |
| Multi-task learning in the computerized diagnosis of breast cancer on DCE-MRIs N Antropova, B Huynh, M Giger arXiv preprint arXiv:1701.03882, 2017 | 1 | 2017 |
| Aspartame exposures in the US population: Demonstration of a novel approach for exposure estimates to food additives using NHANES data LE Riess, BQ Huynh, KE Nachman Journal of Exposure Science & Environmental Epidemiology, 1-11, 2024 | | 2024 |
