GitHub repo for the paper "Low-Rank Adaptations for increased Generalization in Foundation Model features".
https://dsb-ifi.github.io/DoRA-for-FM-robustness/
Use tools/slide_level_tasks/crossvalidation_external.py to run experiments with distinct train/test datasets.
Then change parameters in conf/slide_level_task/cross_validation/config_extreme.yaml and conf/slide_level_task/cross_validation/task/os_prediction_extreme.yaml for different models/datasets/hyperparameters.
Use tools/slide_level_tasks/cross_validation.py for internal validation experiments.
A large part of this codebase is derived from Owkin/HistoSSLscaling (Phikon). Dino tuning is derived from facebookresearch/dino
This page includes material from the project:
Filiot et al. (2023). Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling. medRxiv.
DOI: 10.1101/2023.07.21.23292757
Source: Phikon GitHub Repository
License: Owkin Non-Commercial License
This material is licensed for Non-Commercial use only. You may reproduce and share it, or share any derivative work, under the same Non-Commercial terms and with attribution to the original authors.
If you reuse or adapt this material, please also cite the Phikon paper:
Alexandre Filiot, Ridouane Ghermi, Antoine Olivier, Paul Jacob, Lucas Fidon, Axel Camara, Alice Mac Kain, Charlie Saillard, and Jean-Baptiste Schiratti. Scaling selfsupervised learning for histopathology with masked image modeling. medRxiv, pages
2023–07, 2023.