| Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation T Nair, D Precup, DL Arnold, T Arbel Medical image analysis 59, 101557, 2020 | 513 | 2020 |
| Propagating uncertainty across cascaded medical imaging tasks for improved deep learning inference R Mehta, T Christinck, T Nair, A Bussy, S Premasiri, M Costantino, ... IEEE Transactions on Medical Imaging 41 (2), 360-373, 2021 | 31 | 2021 |
| Propagating uncertainty across cascaded medical imaging tasks for improved deep learning inference R Mehta, T Christinck, T Nair, P Lemaitre, D Arnold, T Arbel Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and …, 2019 | 19 | 2019 |
| Rapid inundation mapping using the US National Water Model, satellite observations, and a convolutional neural network JM Frame, T Nair, V Sunkara, P Popien, S Chakrabarti, T Anderson, ... Geophysical Research Letters 51 (17), e2024GL109424, 2024 | 1 | 2024 |
| 1357P A deep radiomics approach to assess PD-L1 expression and clinical outcomes in patients with advanced non-small cell lung cancer treated with immune checkpoint inhibitors … M Tonneau, K Phan, S Kazandjian, A Elkrief, J Panasci, C Richard, ... Annals of Oncology 32, S1029, 2021 | 1 | 2021 |
| Adversarially learned mixture model A Jesson, C Low-Kam, T Nair, F Soudan, F Chandelier, N Chapados arXiv preprint arXiv:1807.05344, 2018 | 1 | 2018 |
| Improving Digital Representations of Inundation with Deep Learning: from satellite data fusion in Bangladesh to the National Water Model-Satellite fusion in CONUS B Tellman, J Giezendanner, A Saunders, A Islam, AS Islam, S Chakrabarti, ... AGU23, 2024 | | 2024 |
| Addressing and understanding compound flood risk-from floodplain development to flood injustice-with satellites and machine learning B Tellman, J Giezendanner, Z Zhang, JM Frame, L Belury, H Friedrich, ... AGU Fall Meeting Abstracts 2023, GC32A-03, 2023 | | 2023 |
| Flood maps across CONUS using the US National Water Model, satellite observations and convolutional neural networks JM Frame, V Sunkara, T Nair, P Popien, M Goodman, S Chakrabarti, ... AGU Fall Meeting Abstracts 2022, H26B-06, 2022 | | 2022 |
| A Remote Sensing Approach to Bridging the Gaps in FEMA Flood Risk Maps A Lawal, B Tellman, JM Frame, N Leach, T Nair, T Anderson, V Sunkara AGU Fall Meeting Abstracts 2022, NH35C-0504, 2022 | | 2022 |
| Intelligent flood maps: combining satellite observations with hydrologic modeling for high temporal resolution flood maps JM Frame, V Sunkara, S Chakrabarti, C Doyle, M Goodman, T Nair, ... Frontiers in Hydrology 2022, 141-04, 2022 | | 2022 |
| 45 years of sub-daily inundation mapping with deep learning merging the US National Water Model, satellite observations, and USGS stream gauges across CONUS B Tellman, P Popien, T Nair, JM Frame, S Chakrabarti, C Doyle, N Leach, ... AGU24, 0 | | |
| Deep Hydrology: Hourly, Gap-Free Flood Maps Through Joint Satellite and Hydrologic Modelling TNVSJ Frame, P Popien, S Chakrabarti | | |
