| SoftSeg: Advantages of soft versus binary training for image segmentation C Gros, A Lemay, J Cohen-Adad Medical image analysis 71, 102038, 2021 | 70 | 2021 |
| Fair conformal predictors for applications in medical imaging C Lu, A Lemay, K Chang, K Höbel, J Kalpathy-Cramer Proceedings of the AAAI Conference on Artificial Intelligence 36 (11), 12008 …, 2022 | 67 | 2022 |
| Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning A Lemay, C Gros, Z Zhuo, J Zhang, Y Duan, J Cohen-Adad, Y Liu NeuroImage: Clinical 31, 102766, 2021 | 42 | 2021 |
| Improving the repeatability of deep learning models with Monte Carlo dropout A Lemay, K Hoebel, CP Bridge, B Befano, S De Sanjosé, D Egemen, ... npj Digital Medicine 5 (1), 174, 2022 | 33 | 2022 |
| Artificial intelligence–based image analysis in clinical testing: lessons from cervical cancer screening D Egemen, RB Perkins, LC Cheung, B Befano, AC Rodriguez, K Desai, ... JNCI: Journal of the National Cancer Institute 116 (1), 26-33, 2024 | 24 | 2024 |
| Kidney recognition in CT using YOLOv3 A Lemay arXiv preprint arXiv:1910.01268, 2019 | 23 | 2019 |
| Ivadomed: A medical imaging deep learning toolbox C Gros, A Lemay, O Vincent, L Rouhier, A Bucquet, JP Cohen, ... arXiv preprint arXiv:2010.09984, 2020 | 20 | 2020 |
| Reproducible and clinically translatable deep neural networks for cervical screening SR Ahmed, B Befano, A Lemay, D Egemen, AC Rodriguez, S Angara, ... Scientific reports 13 (1), 21772, 2023 | 17 | 2023 |
| Label fusion and training methods for reliable representation of inter-rater uncertainty A Lemay, C Gros, EN Karthik, J Cohen-Adad arXiv preprint arXiv:2202.07550, 2022 | 12 | 2022 |
| Evaluating subgroup disparity using epistemic uncertainty in mammography C Lu, A Lemay, K Hoebel, J Kalpathy-Cramer arXiv preprint arXiv:2107.02716, 2021 | 12 | 2021 |
| Focal loss improves repeatability of deep learning models SR Ahmed, A Lemay, KV Hoebel, J Kalpathy-Cramer Medical Imaging with Deep Learning, 2022 | 9 | 2022 |
| Benefits of linear conditioning for segmentation using metadata A Lemay, C Gros, O Vincent, Y Liu, JP Cohen, J Cohen-Adad Medical Imaging with Deep Learning, 416-430, 2021 | 6 | 2021 |
| Reproducible and clinically translatable deep neural networks for cancer screening SR Ahmed, B Befano, A Lemay, D Egemen, AC Rodriguez, S Angara, ... Research Square, 2023 | 5 | 2023 |
| Benefits of linear conditioning with metadata for image segmentation A Lemay, C Gros, O Vincent, Y Liu, JP Cohen, J Cohen-Adad arXiv preprint arXiv:2102.09582, 2021 | 5 | 2021 |
| Do I know this? segmentation uncertainty under domain shift K Hoebel, C Bridge, A Lemay, K Chang, J Patel, B Rosen, ... Medical Imaging 2022: Image Processing 12032, 261-276, 2022 | 4 | 2022 |
| Proceedings of the AAAI Conference on Artificial Intelligence C Lu, A Lemay, K Chang, K Höbel, J Kalpathy‐Cramer AAAI Press 36 (1), 1872-1880, 2022 | 4 | 2022 |
| Team neuropoly: description of the pipelines for the MICCAI 2021 MS new lesions segmentation challenge U Macar, EN Karthik, C Gros, A Lemay, J Cohen-Adad arXiv preprint arXiv:2109.05409, 2021 | 2 | 2021 |
| Monte Carlo dropout increases model repeatability A Lemay, K Hoebel, CP Bridge, D Egemen, AC Rodriguez, M Schiffman, ... arXiv preprint arXiv:2111.06754, 2021 | 1 | 2021 |
| Monte Carlo dropout for increased deep learning repeatability and disease classification performance in retinopathy of prematurity AS Coyner, A Lemay, K Hoebel, P Singh, S Ostmo, MF Chiang, ... Investigative Ophthalmology & Visual Science 64 (8), 5124-5124, 2023 | | 2023 |
| A generalized framework to predict continuous scores from medical ordinal labels KV Hoebel, A Lemay, JP Campbell, S Ostmo, MF Chiang, CP Bridge, ... arXiv preprint arXiv:2305.19097, 2023 | | 2023 |
