| Comparison of deep transfer learning strategies for digital pathology R Mormont, P Geurts, R Marée Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 161 | 2018 |
| Multi-task pre-training of deep neural networks for digital pathology R Mormont, P Geurts, R Marée IEEE journal of biomedical and health informatics 25 (2), 412-421, 2020 | 62 | 2020 |
| BIAFLOWS: A collaborative framework to benchmark bioimage analysis workflows U Rubens, R Mormont, V Baecker, G Michiels, L Paavolainen, G Ball, ... https://www.biorxiv.org/content/10.1101/707489v1.abstract, 2019 | 39* | 2019 |
| Relieving pixel-wise labeling effort for pathology image segmentation with self-training R Mormont, M Testouri, R Marée, P Geurts European Conference on Computer Vision, 577-592, 2022 | 2 | 2022 |
| A workflow for large scale computer-aided cytology and its applications R Mormont Université de Liège, Liège, Belgique, 2016 | 1 | 2016 |
| Addressing Data Scarcity with Deep Transfer Learning and Self-Training in Digital Pathology R Mormont PQDT-Global, 2022 | | 2022 |
| SLDC: an open-source workflow for object detection in multi-gigapixel images R Mormont, JM Begon, R Hoyoux, R Marée The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), 2016 | | 2016 |
| A Workflow For Computer-Aided Cytology In Whole Slide Images: Application In Fine-Needle Aspiration Thyroid Cytology R MarÃ, R Mormont, JM Begon, C Degand, I Salmon Diagnostic Pathology 1 (8), 2016 | | 2016 |
| Supplementary material: comparison of deep transfer learning strategies for digital pathology R Mormont, P Geurts, R Marée | | |
Raphaël MaréeMontefiore Institute - University of Liège在 uliege.be 的电子邮件经过验证
