| Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer BE Bejnordi, M Veta, PJ Van Diest, B Van Ginneken, N Karssemeijer, ... Jama 318 (22), 2199-2210, 2017 | 3113 | 2017 |
| Data-analysis strategies for image-based cell profiling JC Caicedo, S Cooper, F Heigwer, S Warchal, P Qiu, C Molnar, ... Nature methods 14 (9), 849-863, 2017 | 660 | 2017 |
| Classifying and segmenting microscopy images with deep multiple instance learning OZ Kraus, JL Ba, BJ Frey Bioinformatics 32 (12), i52-i59, 2016 | 494 | 2016 |
| Automated analysis of high‐content microscopy data with deep learning OZ Kraus, BT Grys, J Ba, Y Chong, BJ Frey, C Boone, BJ Andrews Molecular systems biology 13 (4), 924, 2017 | 300 | 2017 |
| Machine learning and computer vision approaches for phenotypic profiling BT Grys, DS Lo, N Sahin, OZ Kraus, Q Morris, C Boone, BJ Andrews Journal of Cell Biology 216 (1), 65-71, 2017 | 174 | 2017 |
| Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting AX Lu, OZ Kraus, S Cooper, AM Moses PLoS computational biology 15 (9), e1007348, 2019 | 98 | 2019 |
| Exploring whole-genome duplicate gene retention with complex genetic interaction analysis E Kuzmin, B VanderSluis, AN Nguyen Ba, W Wang, EN Koch, M Usaj, ... Science 368 (6498), eaaz5667, 2020 | 90 | 2020 |
| Computer vision for high content screening OZ Kraus, BJ Frey Critical reviews in biochemistry and molecular biology 51 (2), 102-109, 2016 | 54 | 2016 |
| Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer B Ehteshami Bejnordi, M Veta, P Johannes van Diest, B van Ginneken, ... Jama 318 (22), 2199-2210, 2017 | 31 | 2017 |
| Integrating images from multiple microscopy screens reveals diverse patterns of change in the subcellular localization of proteins AX Lu, YT Chong, IS Hsu, B Strome, LF Handfield, O Kraus, BJ Andrews, ... Elife 7, e31872, 2018 | 29 | 2018 |
| Rxrx1: A dataset for evaluating experimental batch correction methods M Sypetkowski, M Rezanejad, S Saberian, O Kraus, J Urbanik, J Taylor, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 25 | 2023 |
| System and method for classifying and segmenting microscopy images with deep multiple instance learning O Kraus, BA Jimmy, B Frey US Patent 10,303,979, 2019 | 18 | 2019 |
| Developing deep learning applications for life science and pharma industry D Siegismund, V Tolkachev, S Heyse, B Sick, O Duerr, S Steigele Drug research 68 (06), 305-310, 2018 | 13 | 2018 |
| Rxrx3: Phenomics map of biology MM Fay, O Kraus, M Victors, L Arumugam, K Vuggumudi, J Urbanik, ... Biorxiv, 2023.02. 07.527350, 2023 | 11 | 2023 |
| Masked autoencoders are scalable learners of cellular morphology O Kraus, K Kenyon-Dean, S Saberian, M Fallah, P McLean, J Leung, ... arXiv preprint arXiv:2309.16064, 2023 | 7 | 2023 |
| Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle A Litsios, BT Grys, OZ Kraus, H Friesen, C Ross, MPD Masinas, ... Cell 187 (6), 1490-1507. e21, 2024 | 5 | 2024 |
| Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology O Kraus, K Kenyon-Dean, S Saberian, M Fallah, P McLean, J Leung, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 2 | 2024 |
| Development of a microfluidic platform to investigate effect of dissolved gases on small blood vessel function OZ Kraus University of Toronto (Canada), 2012 | 2 | 2012 |
| Learning Biology Through Puzzle-solving: Unbiased Automatic Understanding of Microscopy Images with Self-supervised Learning A Lu, O Kraus, S Cooper, A Moses Microscopy and Microanalysis 26 (S2), 690-692, 2020 | 1 | 2020 |
| Single-cell imaging of protein dynamics of paralogs reveals mechanisms of gene retention R Dandage, M Papkov, BM Greco, D Fishman, H Friesen, K Wang, ... bioRxiv, 2023 | | 2023 |
Brenda AndrewsDirector, The Donnelly Centre, University of TorontoVerified email at utoronto.ca
