| SLIC superpixels compared to state-of-the-art superpixel methods R Achanta, A Shaji, K Smith, A Lucchi, P Fua, S Süsstrunk IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11), 2274 …, 2012 | 13083* | 2012 |
| Digital image analysis in breast pathology – from image processing techniques to artificial intelligence S Robertson, H Azizpour, K Smith, J Hartman Translational Research 194, 19-35, 2018 | 402 | 2018 |
| Supervoxel-based segmentation of mitochondria in EM image stacks with learned shape features A Lucchi, K Smith, R Achanta, G Knott, P Fua IEEE Transactions on Medical Imaging 31 (2), 474-486, 2012 | 372 | 2012 |
| Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study K Dembrower, Y Wåhlin, Erik, Liu, M Salim, K Smith, P Lindholm, ... The Lancet Digital Health 2 (9), e468-e474, 2020 | 337 | 2020 |
| Bayesian Uncertainty Estimation for Batch Normalized Deep Networks M Teye, H Azizpour, K Smith International Conference on Machine Learning (ICML), 2018 | 336 | 2018 |
| Using particles to track varying numbers of interacting people K Smith, D Gatica-Perez, JM Odobez Computer Vision and Pattern Recognition (CVPR) 1, 962-969, 2005 | 307 | 2005 |
| nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer R Hollandi, A Szkalisity, T Toth, E Tasnadi, C Molnar, B Mathe, I Grexa, ... Cell Systems 4 (003), 2020 | 298* | 2020 |
| External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms M Salim, E Wåhlin, K Dembrower, E Azavedo, T Foukakis, Y Liu, K Smith, ... JAMA Oncology, 2020 | 291 | 2020 |
| Toward robust mammography-based models for breast cancer risk A Yala, PG Mikhael, F Strand, G Lin, K Smith, YL Wan, L Lamb, K Hughes, ... Science Translational Medicine 13 (578), eaba4373, 2021 | 282 | 2021 |
| Is it Time to Replace CNNs with Transformers for Medical Images? C Matsoukas, JF Haslum, M Söderberg, K Smith arXiv preprint arXiv:2108.09038, 2021 | 258 | 2021 |
| Evaluating multi-object tracking K Smith, D Gatica-Perez, JM Odobez, S Ba Computer Vision and Pattern Recognition (CVPR)-Workshops, 36-36, 2005 | 242 | 2005 |
| Deep learning is combined with massive-scale citizen science to improve large-scale image classification DP Sullivan, CF Winsnes, L Åkesson, M Hjelmare, M Wiking, R Schutten, ... Nature biotechnology 36 (9), 820-828, 2018 | 241 | 2018 |
| CIDRE: an illumination-correction method for optical microscopy K Smith, Y Li, F Piccinini, G Csucs, C Balazs, A Bevilacqua, P Horvath Nature Methods 12 (5), 404-406, 2015 | 203 | 2015 |
| Tracking the visual focus of attention for a varying number of wandering people K Smith, SO Ba, JM Odobez, D Gatica-Perez IEEE transactions on pattern analysis and machine intelligence 30 (7), 1212-1229, 2008 | 188 | 2008 |
| Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction K Dembrower, Y Liu, H Azizpour, M Eklund, K Smith, P Lindholm, F Strand Radiology 294 (2), 265-272, 2020 | 181 | 2020 |
| What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factors C Matsoukas, JF Haslum, M Sorkhei, M Söderberg, K Smith IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9225-9234, 2022 | 174 | 2022 |
| Detecting abandoned luggage items in a public space K Smith, P Quelhas, D Gatica-Perez Computer Vision and Pattern Recognition (CVPR)-Workshops, 2006 | 160 | 2006 |
| A fully automated approach to segmentation of irregularly shaped cellular structures in EM images A Lucchi, K Smith, R Achanta, V Lepetit, P Fua MICCAI - International Conference on Medical Image Computing and Computer …, 2010 | 139 | 2010 |
| Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment F Christiansen, EL Epstein, E Smedberg, M Åkerlund, K Smith, E Epstein Ultrasound in Obstetrics & Gynecology 57 (1), 155-163, 2021 | 138 | 2021 |
| Advanced cell classifier: user-friendly machine-learning-based software for discovering phenotypes in high-content imaging data F Piccinini, T Balassa, A Szkalisity, C Molnar, L Paavolainen, K Kujala, ... Cell systems 4 (6), 651-655. e5, 2017 | 105 | 2017 |
Pascal FuaProfessor Computer Science, EPFLVerified email at epfl.ch
