Publikationer av Kevin Smith
Refereegranskade
Artiklar
[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, s. 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, s. 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, s. 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, s. 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, s. 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, s. 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, s. 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, s. 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, s. 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, s. 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.
Konferensbidrag
[19]
E. Konuk et al., "A framework for assessing joint human-AI systems based on uncertainty estimation," i MICCAI2024, 27th INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING, AND COMPUTER ASSISTED INTERVENTION, MARRAKESH, October 6-10, 2024, 2024.
[20]
J. P. Huix et al., "Are Natural Domain Foundation Models Useful for Medical Image Classification?," i Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024, 2024, s. 7619-7628.
[21]
J. Fredin Haslum et al., "Bridging Generalization Gaps in High Content Imaging Through Online Self-Supervised Domain Adaptation," i Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2024,, 2024, s. 7723-7732.
[22]
E. Konuk et al., "Learning from Offline Foundation Features with Tensor Augmentations," i NeurIPS 2024, the Thirty-Eighth Annual Conference on Neural Information Processing Systems, Vancouver, December 10-15, 2024, 2024.
[23]
J. Fredin Haslum et al., "Metadata-guided Consistency Learning for High Content Images," i PLMR: Volume 227: Medical Imaging with Deep Learning, 10-12 July 2023, Nashville, TN, USA, 2023.
[24]
J. Fredin Haslum et al., "Metadata-guided Consistency Learning for High Content Images," i Medical Imaging with Deep Learning 2023, MIDL 2023, 2023, s. 918-936.
[25]
J. Fredin Haslum et al., "Metadata-guided Consistency Learning for High Content Images," i Medical Imaging With Deep Learning, Vol 227, 2023, s. 918-936.
[26]
Y. Liu et al., "PatchDropout : Economizing Vision Transformers Using Patch Dropout," i 2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2023, s. 3942-3951.
[27]
C. Matsoukas et al., "What Makes Transfer Learning Work for Medical Images : Feature Reuse & Other Factors," i 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2022, s. 9215-9224.
[28]
L. A. Van der Goten och K. Smith, "Wide-Range MRI Artifact Removal with Transformers," i BMVC 2022 - 33rd British Machine Vision Conference Proceedings, 2022.
[29]
M. Sorkhei et al., "CSAW-M : An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer," i Conference on Neural Information Processing Systems (NeurIPS) – Datasets and Benchmarks Proceedings, 2021., 2021.
[30]
L. A. Van der Goten et al., "Conditional De-Identification of 3D Magnetic Resonance Images," i 32nd British Machine Vision Conference, BMVC 2021, 2021.
[31]
Y. Liu et al., "Decoupling Inherent Risk and Early Cancer Signs in Image-Based Breast Cancer Risk Models," i 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, s. 230-240.
[32]
F. Baldassarre et al., "Explanation-Based Weakly-Supervised Learning of Visual Relations with Graph Networks," i Proceedings, Part XXVIII Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, 2020, s. 612-630.
[33]
E. Konuk och K. Smith, "An empirical study of the relation between network architecture and complexity," i Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2019, s. 4597-4599.
[34]
M. Teye, H. Azizpour och K. Smith, "Bayesian Uncertainty Estimation for Batch Normalized Deep Networks," i 35th International Conference on Machine Learning, ICML 2018, 2018.
[35]
S. Carlsson et al., "The Preimage of Rectifier Network Activities," i International Conference on Learning Representations (ICLR), 2017.
Icke refereegranskade
Kapitel i böcker
[36]
B. Sirmacek et al., "The Potential of Artificial Intelligence for Achieving Healthy and Sustainable Societies," i The Ethics of Artificial Intelligence for the Sustainable Development Goals, Francesca Mazzi, Luciano Floridi red., : Springer Nature, 2023, s. 65-96.
Övriga
[37]
Y. Liu et al., "Selecting Women for Supplemental Breast Imaging using AI Biomarkers of Cancer Signs, Masking, and Risk," (Manuskript).
[38]
F. Christiansen et al., "International multicenter validation of AI-driven ultrasound detection of ovarian cancer," (Manuskript).
Senaste synkning med DiVA:
2024-10-19 01:02:21