Adaptation of the FM

The FM was adapted by either fine-tuning all its weights or by freezing its weights and adding a linear model on top.

Adaptation through fine-tuning

We provide the YAML configuration for this at experiments/adaptation/fmcib_finetune.yaml.

By default, we configure this for Task 1. You can adapt this for Task 2 and Task 3 by searching for Note: comments in the YAML that outline what must be changed. Make sure you download the weights for the pre-trained foundation model before attempting to reproduce this training.

You can start training by running this in the root code folder,

lighter fit --config_file ./experiments/adaptation/fmcib_finetune.yaml

Adaptation through linear evaluation

4096 features from the FM were for each data point and used to train a logistic regression model using the scikit-learn framework. A comprehensive parameter search for the logistic regression model was performed using the Optuna hyper-parameter optimization framework. The code and utilities for performing the logistic regression modelling is provided in experiments/adaptation/linear

In order to perform the modelling, you can run

cd experiments/adaptation
python run.py <features_folder> <label>

The must contain train_features.csv, val_features.csv and test_features.csv all extracted from our foundation model. The process to extract features from our foundation model is highlighted in this section

The corresponds to the column in the csv files that contains the supervised label to predict. For example, in use-case 2 the label is malignancy.

You can also provide scoring metrics, for instance, using --scoring roc_auc where the scoring metric is a sklearn scorer. You can also provide the number of trials the optimization framework needs to be run for using --trials.

The features folder is provided under outputs/foundation_features to try our the modelling process. Refer here