| Deep learning: RNNs and LSTM R DiPietro, GD Hager Handbook of medical image computing and computer assisted intervention, 503-519, 2020 | 326 | 2020 |
| Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses C Rupprecht, I Laina, R DiPietro, M Baust, F Tombari, GD Hager, N Navab International Conference on Computer Vision, 2017 | 230 | 2017 |
| Long short-term memory kalman filters: Recurrent neural estimators for pose regularization H Coskun, F Achilles, R DiPietro, N Navab, F Tombari Proceedings of the IEEE International Conference on Computer Vision, 5524-5532, 2017 | 229 | 2017 |
| Recognizing surgical activities with recurrent neural networks R DiPietro, C Lea, A Malpani, N Ahmidi, SS Vedula, GI Lee, MR Lee, ... Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th …, 2016 | 166 | 2016 |
| Long-wave infrared hyperspectral remote sensing of chemical clouds: A focus on signal processing approaches D Manolakis, S Golowich, RS DiPietro IEEE Signal Processing Magazine 31 (4), 120-141, 2014 | 77 | 2014 |
| Segmenting and classifying activities in robot-assisted surgery with recurrent neural networks R DiPietro, N Ahmidi, A Malpani, M Waldram, GI Lee, MR Lee, SS Vedula, ... International journal of computer assisted radiology and surgery 14 (11 …, 2019 | 64 | 2019 |
| Determining magnetic nanoparticle size distributions from thermomagnetic measurements RS DiPietro, HG Johnson, SP Bennett, TJ Nummy, LH Lewis, D Heiman Applied Physics Letters 96 (22), 2010 | 49 | 2010 |
| Hyperspectral matched filter with false-alarm mitigation RS DiPietro, DG Manolakis, RB Lockwood, T Cooley, J Jacobson Optical Engineering 51 (1), 016202-016202, 2012 | 43 | 2012 |
| Thermomagnetic determination of Fe3O4 magnetic nanoparticle diameters for biomedical applications BD Plouffe, DK Nagesha, RS DiPietro, S Sridhar, D Heiman, SK Murthy, ... Journal of magnetism and magnetic materials 323 (17), 2310-2317, 2011 | 37 | 2011 |
| Large low field magnetoresistance in La0. 67Sr0. 33MnO3 nanowire devices B Jugdersuren, S Kang, RS DiPietro, D Heiman, D McKeown, I Pegg, ... Journal of Applied Physics 109 (1), 2011 | 36 | 2011 |
| Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies R DiPietro, C Rupprecht, N Navab, GD Hager International Conference on Learning Representations, Workshop Track, 2018 | 35 | 2018 |
| Unsupervised learning for surgical motion by learning to predict the future R DiPietro, GD Hager International conference on medical image computing and computer-assisted …, 2018 | 28 | 2018 |
| Performance evaluation of hyperspectral detection algorithms for subpixel objects RS DiPietro, D Manolakis, R Lockwood, T Cooley, J Jacobson Algorithms and technologies for multispectral, hyperspectral, and …, 2010 | 27 | 2010 |
| Scene embedding for visual navigation AK Brown, RS DiPietro US Patent 10,902,616, 2021 | 26 | 2021 |
| Automated surgical activity recognition with one labeled sequence R DiPietro, GD Hager Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 22 | 2019 |
| Automated surgical-phase recognition using rapidly-deployable sensors R DiPietro, R Stauder, E Kayis, A Schneider, M Kranzfelder, H Feussner, ... Proc MICCAI Workshop M2CAI, 2015 | 14 | 2015 |
| Deep learning: RNNs and LSTM. Handbook of Medical Image Computing and Computer Assisted Intervention, 503–519 R DiPietro, GD Hager | 13 | 2019 |
| Neural network training using robust temporal ensembling AK Brown, RS DiPietro, BD Schifferer US Patent App. 17/033,147, 2022 | 11 | 2022 |
| International conference on medical image computing and computer-assisted intervention R DiPietro, C Lea, A Malpani, N Ahmidi, SS Vedula, GI Lee, MR Lee, ... Springer, 2016 | 8 | 2016 |
| False-alarm characterization in hyperspectral gas-detection applications RS DiPietro, E Truslow, DG Manolakis, SE Golowich, RB Lockwood Imaging Spectrometry XVII 8515, 138-148, 2012 | 7 | 2012 |
