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Publications by Kevin Smith

Refereegranskade

Artiklar

[2]
[4]
A. Yala et al., "Toward robust mammography-based models for breast cancer risk," Science Translational Medicine, vol. 13, no. 578, 2021.
[9]
[11]
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.
[12]
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.
[13]
C. Brasko et al., "Intelligent image-based in situ single-cell isolation," Nature Communications, vol. 9, 2018.
[16]
[17]
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

[18]
J. Fredin Haslum et al., "Bridging Generalization Gaps in High Content Imaging Through Online Self-Supervised Domain Adaptation," in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2024, pp. 7723-7732, 2024.
[19]
J. Fredin Haslum et al., "Metadata-guided Consistency Learning for High Content Images," in PLMR: Volume 227: Medical Imaging with Deep Learning, 10-12 July 2023, Nashville, TN, USA, 2023.
[20]
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.
[21]
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.
[22]
L. A. Van der Goten and K. Smith, "Wide-Range MRI Artifact Removal with Transformers," in BMVC 2022 - 33rd British Machine Vision Conference Proceedings, 2022.
[23]
M. Sorkhei et al., "CSAW-M : An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer," in Conference on Neural Information Processing Systems (NeurIPS) – Datasets and Benchmarks Proceedings, 2021., 2021.
[24]
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.
[25]
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.
[26]
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.
[27]
M. Teye, H. Azizpour and K. Smith, "Bayesian Uncertainty Estimation for Batch Normalized Deep Networks," in 35th International Conference on Machine Learning, ICML 2018, 2018.
[28]
S. Carlsson et al., "The Preimage of Rectifier Network Activities," in International Conference on Learning Representations (ICLR), 2017.

Icke refereegranskade

Kapitel i böcker

[29]
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.
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2024-05-19 02:51:41