Publications by Kevin Smith
Refereegranskade
Artiklar
[1]
A. Yala et al., "Toward robust mammography-based models for breast cancer risk," Science Translational Medicine, vol. 13, no. 578, 2021.
[2]
F. Christiansen et al., "Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors : comparison with expert subjective assessment," Ultrasound in Obstetrics and Gynecology, vol. 57, no. 1, pp. 155-163, 2021.
[3]
K. Dembrower et al., "Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction," Radiology, vol. 294, no. 2, pp. 265-272, 2020.
[4]
K. Dembrower et al., "Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload : a retrospective simulation study," The Lancet Digital Health, vol. 2, no. 9, pp. E468-E474, 2020.
[5]
M. Salim et al., "External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms," JAMA Oncology, vol. 6, no. 10, pp. 1581, 2020.
[6]
R. Hollandi et al., "nucleAIzer : A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer," Cell Systems, vol. 10, no. 5, pp. 453-458.e6, 2020.
[7]
A. Casanova et al., "A Role for the VPS Retromer in Brucella Intracellular Replication Revealed by Genomewide siRNA Screening," mSphere, vol. 4, no. 3, 2019.
[8]
D. P. Sullivan et al., "Deep learning is combined with massive-scale citizen science to improve large-scale image classification," Nature Biotechnology, vol. 36, no. 9, pp. 820-+, 2018.
[9]
S. Robertson et al., "Digital image analysis in breast pathology-from image processing techniques to artificial intelligence," Translational Research : The Journal of Laboratory and Clinical Medicine, vol. 194, pp. 19-35, 2018.
[10]
C. Brasko et al., "Intelligent image-based in situ single-cell isolation," Nature Communications, vol. 9, 2018.
[11]
K. Smith et al., "Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays," CELL SYSTEMS, vol. 6, no. 6, pp. 636-653, 2018.
[12]
F. Piccinini et al., "Advanced Cell Classifier : User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data," CELL SYSTEMS, vol. 4, no. 6, pp. 651-+, 2017.
[13]
L. Fusco et al., "Computer vision profiling of neurite outgrowth dynamics reveals spatio-temporal modularity of Rho GTPase signaling," Journal of Cell Biology, vol. 212, no. 1, pp. 91-111, 2016.
[14]
C. F. Winsnes et al., "Multi-label prediction of subcellular localization in confocal images using deep neural networks," Molecular Biology of the Cell, vol. 27, no. 25, 2016.
Konferensbidrag
[15]
Y. Liu et al., "PatchDropout : Economizing Vision Transformers Using Patch Dropout," in 2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2023, pp. 3942-3951.
[16]
C. Matsoukas et al., "What Makes Transfer Learning Work for Medical Images : Feature Reuse & Other Factors," in 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2022, pp. 9215-9224.
[17]
Y. Liu et al., "Decoupling Inherent Risk and Early Cancer Signs in Image-Based Breast Cancer Risk Models," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 : 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI (Lecture Notes in Computer Science), 2020, pp. 230-240.
[18]
F. Baldassarre et al., "Explanation-Based Weakly-Supervised Learning of Visual Relations with Graph Networks," in Proceedings, Part XXVIII Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, 2020, pp. 612-630.
[19]
E. Konuk and K. Smith, "An empirical study of the relation between network architecture and complexity," in Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2019, pp. 4597-4599.
[20]
M. Teye, H. Azizpour and K. Smith, "Bayesian Uncertainty Estimation for Batch Normalized Deep Networks," in International Conference on Machine Learning (ICML), 2018.
[21]
S. Carlsson et al., "The Preimage of Rectifier Network Activities," in International Conference on Learning Representations (ICLR), 2017.
Icke refereegranskade
Kapitel i böcker
[22]
B. Sirmacek et al., "The Potential of Artificial Intelligence for Achieving Healthy and Sustainable Societies," in The Ethics of Artificial Intelligence for the Sustainable Development Goals, Francesca Mazzi, Luciano Floridi Ed., : Springer Nature, 2023, pp. 65-96.
Senaste synkning med DiVA:
2023-09-15 00:31:51