Self-Supervised Graph Representation Learning
Event details
| Date | 23.08.2023 |
| Hour | 10:00 › 12:00 |
| Speaker | Sevda Ögüt |
| Location | |
| Category | Conferences - Seminars |
| Event Language | French, English |
EDIC candidacy exam
Exam president: Prof. Lenka Zdeborová
Thesis advisor: Prof. Pascal Frossard
Thesis co-advisor: Dr. Dorina Thanou
Co-examiner: Prof. Dimitri Van De Ville
Abstract
Coming soon
Background papers
1- DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer (https://www.sciencedirect.com/science/article/abs/pii/S1361841522001116)
2- Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning (https://www.nature.com/articles/s41551-022-00923-0)
3- E(n) Equivariant Graph Neural Networks (http://proceedings.mlr.press/v139/satorras21a/satorras21a.pdf)
Exam president: Prof. Lenka Zdeborová
Thesis advisor: Prof. Pascal Frossard
Thesis co-advisor: Dr. Dorina Thanou
Co-examiner: Prof. Dimitri Van De Ville
Abstract
Coming soon
Background papers
1- DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer (https://www.sciencedirect.com/science/article/abs/pii/S1361841522001116)
2- Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning (https://www.nature.com/articles/s41551-022-00923-0)
3- E(n) Equivariant Graph Neural Networks (http://proceedings.mlr.press/v139/satorras21a/satorras21a.pdf)
Practical information
- General public
- Free