| Deep convolutional neural network for inverse problems in imaging KH Jin, MT McCann, E Froustey, M Unser IEEE transactions on image processing 26 (9), 4509-4522, 2017 | 2125 | 2017 |
| Convolutional neural networks for inverse problems in imaging: A review MT McCann, KH Jin, M Unser IEEE Signal Processing Magazine 34 (6), 85-95, 2017 | 685 | 2017 |
| CNN-based projected gradient descent for consistent CT image reconstruction H Gupta, KH Jin, HQ Nguyen, MT McCann, M Unser IEEE transactions on medical imaging 37 (6), 1440-1453, 2018 | 365 | 2018 |
| Automated histology analysis: Opportunities for signal processing MT McCann, JA Ozolek, CA Castro, B Parvin, J Kovacevic IEEE Signal Processing Magazine 32 (1), 78-87, 2014 | 155 | 2014 |
| Diffusion posterior sampling for general noisy inverse problems H Chung, J Kim, MT Mccann, ML Klasky, JC Ye arXiv preprint arXiv:2209.14687, 2022 | 118 | 2022 |
| CryoGAN: A new reconstruction paradigm for single-particle cryo-EM via deep adversarial learning H Gupta, MT McCann, L Donati, M Unser IEEE Transactions on Computational Imaging 7, 759-774, 2021 | 74 | 2021 |
| Indirect structural health monitoring in bridges: scale experiments F Cerda, J Garrett, J Bielak, P Rizzo, JA Barrera, Z Zhang, S Chen, ... Proc. Int. Conf. Bridge Maint., Safety Manag., Lago di Como, 346-353, 2012 | 56 | 2012 |
| Images as Occlusions of Textures: A Framework for Segmentation MT McCann, DG Mixon, MC Fickus, CA Castro, JA Ozolek, J Kovac̆ević IEEE Transactions on Image Processing 23 (5), 2033 - 2046, 2013 | 53 | 2013 |
| Pocket guide to solve inverse problems with GlobalBioIm E Soubies, F Soulez, MT McCann, T Pham, L Donati, T Debarre, D Sage, ... Inverse Problems 35 (10), 104006, 2019 | 44 | 2019 |
| Algorithm and benchmark dataset for stain separation in histology images MT McCann, J Majumdar, C Peng, CA Castro, J Kovaèeviæ 2014 IEEE International Conference on Image Processing (ICIP), 3953-3957, 2014 | 44 | 2014 |
| Biomedical image reconstruction: From the foundations to deep neural networks MT McCann, M Unser Foundations and Trends® in Signal Processing 13 (3), 283-359, 2019 | 36 | 2019 |
| Fast 3D reconstruction method for differential phase contrast X-ray CT MT McCann, M Nilchian, M Stampanoni, M Unser Optics express 24 (13), 14564-14581, 2016 | 29 | 2016 |
| GlobalBioIm: A unifying computational framework for solving inverse problems M Unser, E Soubies, F Soulez, M McCann, L Donati Computational Optical Sensing and Imaging, CTu1B. 1, 2017 | 28 | 2017 |
| Solving 3d inverse problems using pre-trained 2d diffusion models H Chung, D Ryu, MT McCann, ML Klasky, JC Ye Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 24 | 2023 |
| Electrode subset selection methods for an EEG-based P300 brain-computer interface MT McCann, DE Thompson, ZH Syed, JE Huggins Disability and Rehabilitation: Assistive Technology 10 (3), 216-220, 2015 | 23 | 2015 |
| Automated colitis detection from endoscopic biopsies as a tissue screening tool in diagnostic pathology MT McCann, R Bhagavatula, MC Fickus, JA Ozolek, J Kovaèeviæ 2012 19th IEEE International Conference on Image Processing, 2809-2812, 2012 | 19 | 2012 |
| Tools for automated histology image analysis MT McCann Carnegie Mellon University, 2015 | 17 | 2015 |
| Scientific computational imaging code (SCICO) T Balke, F Davis Rivera, C Garcia-Cardona, S Majee, MT McCann, ... Journal of Open Source Software 7 (LA-UR-22-28555), 2022 | 12 | 2022 |
| Unified supervised-unsupervised (super) learning for x-ray ct image reconstruction S Ye, Z Li, MT McCann, Y Long, S Ravishankar IEEE Transactions on Medical Imaging 40 (11), 2986-3001, 2021 | 11 | 2021 |
| Supervised learning of sparsity-promoting regularizers for denoising MT McCann, S Ravishankar arXiv preprint arXiv:2006.05521, 2020 | 9 | 2020 |
Michael UnserProfessor, Biomedical Imaging Group, Ecole Polytechnique Fédérale de Lausanne (EPFL), SwitzerlandVerified email at epfl.ch
