| A cluster-then-label semi-supervised learning approach for pathology image classification M Peikari, S Salama, S Nofech-Mozes, AL Martel Scientific reports 8 (1), 1-13, 2018 | 173 | 2018 |
| Triaging Diagnostically Relevant Regions from Pathology Whole Slides of Breast Cancer: A Texture Based Approach M Peikari, M Gangeh, J Zubovits, G Clarke, A Martel IEEE Transaction on Medical Imaging, 2015 | 84 | 2015 |
| Automated and manual quantification of tumour cellularity in digital slides for tumour burden assessment S Akbar, M Peikari, S Salama, AY Panah, S Nofech-Mozes, AL Martel Scientific reports 9 (1), 14099, 2019 | 49 | 2019 |
| Automatic cellularity assessment from post‐treated breast surgical specimens M Peikari, S Salama, S Nofech‐Mozes, AL Martel Cytometry Part A 91 (11), 1078-1087, 2017 | 49 | 2017 |
| Transitioning between convolutional and fully connected layers in neural networks S Akbar, M Peikari, S Salama, S Nofech-Mozes, A Martel Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017 | 29 | 2017 |
| Characterization of ultrasound elevation beamwidth artifacts for prostate brachytherapy needle insertion M Peikari, TK Chen, A Lasso, T Heffter, G Fichtinger, EC Burdette Medical physics 39 (1), 246-256, 2012 | 27 | 2012 |
| Assessment of residual breast cancer cellularity after neoadjuvant chemotherapy using digital pathology [data set] AL Martel, S Nofech-Mozes, S Salama, S Akbar, M Peikari The Cancer Imaging Archive, 2019 | 21 | 2019 |
| Clustering analysis for semi-supervised learning improves classification performance of digital pathology M Peikari, J Zubovits, G Clarke, AL Martel Machine Learning in Medical Imaging: 6th International Workshop, MLMI 2015 …, 2015 | 19 | 2015 |
| Automatic cell detection and segmentation from H and E stained pathology slides using colorspace decorrelation stretching M Peikari, AL Martel Medical Imaging 2016: Digital Pathology 9791, 292-297, 2016 | 18 | 2016 |
| The transition module: a method for preventing overfitting in convolutional neural networks S Akbar, M Peikari, S Salama, S Nofech-Mozes, AL Martel Computer Methods in Biomechanics and Biomedical Engineering: Imaging …, 2019 | 14 | 2019 |
| Localization and classification of cell nuclei in post-neoadjuvant breast cancer surgical specimen using fully convolutional networks R Bidart, MJ Gangeh, M Peikari, S Salama, S Nofech-Mozes, AL Martel, ... Medical Imaging 2018: Digital Pathology 10581, 191-198, 2018 | 13 | 2018 |
| Determining tumor cellularity in digital slides using resnet S Akbar, M Peikari, S Salama, S Nofech-Mozes, AL Martel Medical Imaging 2018: Digital Pathology 10581, 233-239, 2018 | 12 | 2018 |
| Effects of ultrasound section-thickness on brachytherapy needle tip localization error M Peikari, TK Chen, A Lasso, T Heffter, G Fichtinger Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011: 14th …, 2011 | 10 | 2011 |
| An ensemble-based approach to the development of clinical prediction models for future-onset heart failure and coronary artery disease using machine learning K Taha, HJ Ross, M Peikari, B Mueller, CPS Fan, E Crowdy, C Manlhiot Journal of the American College of Cardiology 75 (11_Supplement_1), 2046-2046, 2020 | 7 | 2020 |
| Section-thickness profiling for brachytherapy ultrasound guidance M Peikari, TK Chen, EC Burdette, G Fichtinger Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling …, 2011 | 5 | 2011 |
| A Texture Based Approach to Automated Detection of Diagnostically Relevant Regions in Breast Digital Pathology M Peikari, J Zubovits, G Clarcke, AL Martel Medical Image Computing and Computer Assisted Intervention Society (MICCAI …, 2013 | 3 | 2013 |
| Fully Convolutional Networks in Localization and Classification of Cell Nuclei R Bidart, MJ Gangeh, M Peikari, S Salama, S Nofech-Mozes, S Nofech, ... | 1 | 2019 |
| Automatic Cellularity Assessment in Surgical Specimens After Neoadjuvant Therapy of Breast Cancer M Peikari University of Toronto (Canada), 2018 | 1 | 2018 |
| Building Sparse 3D representations from a Set of Calibrated Panoramic Images D Wojtaszek, R Laganiere, H Peikari, M Peikari Symposium on Photogrammetry Computer Vision and Image Analysis 38, 186-191, 0 | 1 | |
| Predicting heart failure outcomes by integrating breath-by-breath measurements from cardiopulmonary exercise testing and clinical data through a deep learning survival neural … HJ Ross, M Peikari, JKK Vishram-Nielsen, CPS Fan, J Hearn, M Walker, ... European Heart Journal-Digital Health, ztae005, 2024 | | 2024 |
