The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation S Jégou, M Drozdzal, D Vazquez, A Romero, Y Bengio Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 2092 | 2017 |
The importance of skip connections in biomedical image segmentation M Drozdzal, E Vorontsov, G Chartrand, S Kadoury, C Pal International workshop on deep learning in medical image analysis …, 2016 | 1385 | 2016 |
The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680, 2023 | 1262 | 2023 |
Deep learning: a primer for radiologists G Chartrand, PM Cheng, E Vorontsov, M Drozdzal, S Turcotte, CJ Pal, ... Radiographics 37 (7), 2113-2131, 2017 | 1192 | 2017 |
fastMRI: An open dataset and benchmarks for accelerated MRI J Zbontar, F Knoll, A Sriram, T Murrell, Z Huang, MJ Muckley, A Defazio, ... arXiv preprint arXiv:1811.08839, 2018 | 924 | 2018 |
A benchmark for endoluminal scene segmentation of colonoscopy images D Vázquez, J Bernal, FJ Sánchez, G Fernández-Esparrach, AM López, ... Journal of healthcare engineering 2017 (1), 4037190, 2017 | 648 | 2017 |
fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning F Knoll, J Zbontar, A Sriram, MJ Muckley, M Bruno, A Defazio, M Parente, ... Radiology: Artificial Intelligence 2 (1), e190007, 2020 | 396 | 2020 |
Learning normalized inputs for iterative estimation in medical image segmentation M Drozdzal, G Chartrand, E Vorontsov, M Shakeri, L Di Jorio, A Tang, ... Medical image analysis 44, 1-13, 2018 | 277 | 2018 |
Inverse cooking: Recipe generation from food images A Salvador, M Drozdzal, X Giró-i-Nieto, A Romero Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 186 | 2019 |
Reducing uncertainty in undersampled MRI reconstruction with active acquisition Z Zhang, A Romero, MJ Muckley, P Vincent, L Yang, M Drozdzal Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 143 | 2019 |
Instance-conditioned gan A Casanova, M Careil, J Verbeek, M Drozdzal, A Romero Soriano Advances in Neural Information Processing Systems 34, 27517-27529, 2021 | 139 | 2021 |
Generic feature learning for wireless capsule endoscopy analysis S Seguí, M Drozdzal, G Pascual, P Radeva, C Malagelada, F Azpiroz, ... Computers in biology and medicine 79, 163-172, 2016 | 118 | 2016 |
Active MR k-space Sampling with Reinforcement Learning L Pineda, S Basu, A Romero, R Calandra, M Drozdzal Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 89 | 2020 |
Parameter prediction for unseen deep architectures B Knyazev, M Drozdzal, GW Taylor, A Romero Soriano Advances in Neural Information Processing Systems 34, 29433-29448, 2021 | 83 | 2021 |
Categorization and segmentation of intestinal content frames for wireless capsule endoscopy S Segui, M Drozdzal, F Vilarino, C Malagelada, F Azpiroz, P Radeva, ... IEEE Transactions on Information Technology in Biomedicine 16 (6), 1341-1352, 2012 | 61 | 2012 |
3d shape reconstruction from vision and touch E Smith, R Calandra, A Romero, G Gkioxari, D Meger, J Malik, M Drozdzal Advances in Neural Information Processing Systems 33, 14193-14206, 2020 | 58 | 2020 |
System and method for displaying motility events in an in vivo image stream M Drozdzal, SS Mesquida, P Radeva, J Vitria, L Igual-Munoz, ... US Patent 9,514,556, 2016 | 51 | 2016 |
On the evaluation of conditional GANs T DeVries, A Romero, L Pineda, GW Taylor, M Drozdzal arXiv preprint arXiv:1907.08175, 2019 | 46 | 2019 |
Convolutional neural networks for mesh-based parcellation of the cerebral cortex G Cucurull, K Wagstyl, A Casanova, P Veličković, E Jakobsen, ... Medical imaging with deep learning, 2018 | 44 | 2018 |
Active 3D shape reconstruction from vision and touch E Smith, D Meger, L Pineda, R Calandra, J Malik, A Romero Soriano, ... Advances in Neural Information Processing Systems 34, 16064-16078, 2021 | 43 | 2021 |