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Kevin Smith
Kevin Smith
Associate Professor, KTH Royal Institute of Technology & Science for Life Laboratory
Verified email at kth.se - Homepage
Title
Cited by
Cited by
Year
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
12219*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
3582018
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
3502012
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
3022005
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
M Teye, H Azizpour, K Smith
International Conference on Machine Learning (ICML), 2018
2942018
Slic superpixels
A Radhakrishna, A Shaji, K Smith, A Lucchi, P Fua, S Susstrunk
Dept. School Comput. Commun. Sci., EPFL, Lausanne, Switzerland, Tech. Rep 149300, 2010
273*2010
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
253*2020
Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study
K Dembrower, Y Wċhlin, Erik, Liu, M Salim, K Smith, P Lindholm, ...
The Lancet Digital Health 2 (9), e468-e474, 2020
2432020
Evaluating multi-object tracking
K Smith, D Gatica-Perez, JM Odobez, S Ba
Computer Vision and Pattern Recognition (CVPR)-Workshops, 36-36, 2005
2402005
External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms
M Salim, E Wċhlin, K Dembrower, E Azavedo, T Foukakis, Y Liu, K Smith, ...
JAMA Oncology, 2020
2352020
Deep learning is combined with massive-scale citizen science to improve large-scale image classification
DP Sullivan, CF Winsnes, L Ċkesson, M Hjelmare, M Wiking, R Schutten, ...
Nature biotechnology 36 (9), 820-828, 2018
2182018
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
2022021
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
1852021
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
1852008
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
1762015
Detecting abandoned luggage items in a public space
K Smith, P Quelhas, D Gatica-Perez
Computer Vision and Pattern Recognition (CVPR)-Workshops, 2006
1542006
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
1502020
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
1312010
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 Ċkerlund, K Smith, E Epstein
Ultrasound in Obstetrics & Gynecology 57 (1), 155-163, 2021
1082021
What Makes Transfer Learning Work For Medical Images: Feature Reuse & Other Factors
C Matsoukas, JF Haslum, M Sorkhei, M Söderberg, K Smith
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
1032022
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