Publications by Kevin Smith
Peer reviewed
Articles
[1]
J. Fredin Haslum et al., "Cell Painting-based bioactivity prediction boosts high-throughput screening hit-rates and compound diversity," Nature Communications, vol. 15, no. 1, 2024.
[2]
Y. Liu et al., "Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening," Radiology, vol. 311, no. 1, 2024.
[3]
M. Salim et al., "Differences and similarities in false interpretations by AI CAD and radiologists in screening mammography," British Journal of Radiology, vol. 96, no. 1151, 2023.
[4]
F. Cossío et al., "VAI-B: A multicenter platform for the external validation of artificial intelligence algorithms in breast imaging," Journal of Medical Imaging, vol. 10, no. 6, 2023.
[5]
A. Yala et al., "Toward robust mammography-based models for breast cancer risk," Science Translational Medicine, vol. 13, no. 578, 2021.
[6]
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.
[7]
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.
[8]
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.
[9]
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.
[10]
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.
[11]
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.
[12]
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.
[13]
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.
[14]
C. Brasko et al., "Intelligent image-based in situ single-cell isolation," Nature Communications, vol. 9, 2018.
[15]
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.
[16]
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.
[17]
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.
[18]
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.
Conference papers
[19]
E. Konuk et al., "A Framework for Assessing Joint Human-AI Systems Based on Uncertainty Estimation," in MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2024, PT X, 2024, pp. 3-12.
[20]
E. Konuk et al., "A framework for assessing joint human-AI systems based on uncertainty estimation," in Miccai2024, 27Th International Conference On Medical Image Computing, And Computer Assisted Intervention, Marrakesh, October 6-10, 2024, 2024.
[21]
J. P. Huix et al., "Are Natural Domain Foundation Models Useful for Medical Image Classification?," in Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024, 2024, pp. 7619-7628.
[22]
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,, 2024, pp. 7723-7732.
[23]
E. Konuk et al., "Learning from Offline Foundation Features with Tensor Augmentations," in Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 2024.
[24]
L. A. Van der Goten and K. Smith, "Privacy Protection in MRI Scans Using 3D Masked Autoencoders," in 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, 2024.
[25]
L. A. Van der Goten and K. Smith, "Privacy Protection in MRI Scans Using 3D Masked Autoencoders," in Medical image computing and computer assisted intervention - MICCAI 2024, pt vii, 2024, pp. 583-592.
[26]
J. Fredin Haslum et al., "Metadata-guided Consistency Learning for High Content Images," in Proceedings of Machine Learning Research, Volume 227: Medical Imaging with Deep Learning, 2023.
[27]
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.
[28]
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.
[29]
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.
[30]
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.
[31]
M. Sorkhei et al., "CSAW-M : An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer," in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, 2021.
[32]
L. A. Van der Goten et al., "Conditional De-Identification of 3D Magnetic Resonance Images," in 32nd British Machine Vision Conference, BMVC 2021, 2021.
[33]
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.
[34]
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.
[35]
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.
[36]
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.
[37]
S. Carlsson et al., "The Preimage of Rectifier Network Activities," in International Conference on Learning Representations (ICLR), 2017.
Non-peer reviewed
Chapters in books
[38]
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.
Other
[39]
Y. Liu et al., "Selecting Women for Supplemental Breast Imaging using AI Biomarkers of Cancer Signs, Masking, and Risk," (Manuscript).
[40]
F. Christiansen et al., "International multicenter validation of AI-driven ultrasound detection of ovarian cancer," (Manuscript).
[41]
L. A. Van der Goten, J. Guo and K. Smith, "MAMOC : MRI Motion Correction via Masked Autoencoding," (Manuscript).
Latest sync with DiVA:
2025-05-11 00:50:51 UTC