Mormont, R., Testouri, M., Marée, R., & Geurts, P. (2022). Relieving pixel-wise labeling effort for pathology image segmentation with self-training. In Lecture Notes in Computer Science. Genève, Switzerland: Springer Cham. doi:10.1007/978-3-031-25082-8_39
Data scarcity is a common issue when training deep learning models for digital pathology, as larg...
Peer reviewed
Mormont, R. (2022). Addressing data scarcity with deep transfer learning and self-training in digital pathology [Doctoral thesis, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/293358
Pathology, the field of medicine and biology interested in studying and diagnosing diseases, is o...
Rubens, U.* , Mormont, R.* , Paavolainen, L., Bäcker, V., Pavie, B., Scholz, L. A., Michiels, G., Maska, M., Ünay, D., Ball, G., Hoyoux, R., Vandaele, R., Golani, O., Stanciu, S. G., Sladoje, N., Paul-Gilloteaux, P., Marée, R.* , & Tosi, S.*. (12 June 2020). BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows. Patterns, 1. doi:10.1016/j.patter.2020.100040
Image analysis is key to extracting quantitative information from scientific microscopy images, b...
Peer reviewed
Mormont, R., Geurts, P., & Marée, R. (2020). Multi-task pre-training of deep neural networks for digital pathology. IEEE Journal of Biomedical and Health Informatics. doi:10.1109/JBHI.2020.2992878
In this work, we investigate multi-task learning as a way of pre-training models for classificati...
Peer Reviewed verified by ORBi
Mormont, R., Geurts, P., & Marée, R. (2018). Comparison of deep transfer learning strategies for digital pathology. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE.
In this paper, we study deep transfer learning as a way of overcoming object recognition challeng...
Peer reviewed
Mormont, R., Begon, J.-M., Hoyoux, R., & Marée, R. (12 September 2016). SLDC: an open-source workflow for object detection in multi-gigapixel images [Paper presentation]. The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), Kortrijk, Belgium.