CLAM

In addition to standard Tensorflow/Keras model applications, slideflow supports training models with CLAM. A slightly modified version of CLAM which supports slideflow dataset and input pipelines is included in slideflow.clam.

Creating slide activations

The first step in the CLAM pipeline is generating tile-level activations across whole-slide images. While the original CLAM paper used features generated from an imagenet-trained model, we have found it useful to generate feature activations from models pretrained with histology images. To this end, the project function slideflow.Project.generate_features_for_clam() accepts any model as input and will generate feature vectors from the specified intermediate layers. For example:

P.generate_features_for_clam(
    model='/path/to/saved/model',
    outdir='/clam/path',
    layers=['postconv']
)

Training

To train a CLAM model, use the project function slideflow.Project.train_clam(). Clam arguments are configured with slideflow.clam.get_args():

dataset = P.dataset(tile_px=299, tile_um=302)
P.generate_features_for_clam(..., outdir='/clam/path')

clam_args = sf.clam.get_args(k=3, bag_loss='svm', ...)

P.train_clam(
    exp_name='test_experiment',
    pt_files='/clam/path',
    outcomes='category1',
    dataset=dataset,
    clam_args=clam_args
)

The training function will, by default, save heatmaps of the attention layers for each of the validation slides. This behavior can be disabled by passing attention_heatmaps=False.

Evaluation

To evaluate a saved CLAM model on an external dataset, first extract features from this dataset, then use the project function slideflow.Project.evaluate_clam():

P.generate_features_for_clam(..., outdir='/eval/clam/path')

P.evaluate_clam(
    exp_name='evaluation',
    pt_files='/eval/clam/path',
    outcomes='category1',
    tile_px=299,
    tile_um=302
)