Configuration file

The configuration file contains various settings and parameters that control the behavior and settings of the project. Refer to the config-template.yaml file for more information.

dataset

Parameter	Type	Description
annot_dir	path	Slide annotations directory path. Should have the same names as that in slide_dir.
create_zip	bool	Bundle the created dataset directory in a ZIP for easier download.
data_dir_name	str	Used to create dataset/{data_dir_name}/.
downsample_factor	int	Downsample slides resolution by this factor. Defaults to preserve aspect ratio.
downsample_size	tuple[int, int]	Downsample slides to this size.
n_splits	int	Number of splits for cross-validation.
overlap	bool	Overlap factor for extracting patches. Should be between 0 and 1.
patch_size	tuple[int, int]	Patch size for the patches.
save_slides	bool	Whether to save slides, in dataset/{data_dir_name}-slides/.
slide_dir	path	Slides directory path. Corresponding annotations should be in annot_dir.
use_augment	bool	Whether to use data augmentation at patch level for the train split. Preferably always use as True.

gpu

Parameter	Type	Description
device_index	int	Device index for the GPU. Set to -1 to disable GPU and use CPU instead.

heatmaps

Parameter	Type	Description
alpha	float	Heatmap transparency while overlaying on the slide. Should be between 0 and 1.
blur	tuple[int, int]	Gaussian blur kernel size for the heatmap.
cmap	str	Colormap for the heatmap. Refer to matplotlib colormaps.
downsample_factor	int	Downsample slides resolution by this factor (when source_dir is provided).
downsample_size	tuple[int, int]	Downsample slides to this size (when source_dir is provided).
file_extension	str	File extension for the heatmap images to be saved.
invert_preds	bool	Whether to invert the predictions before making the heatmaps. Default is true.
overlap	float	Overlap factor for the heatmap patches. Should be between 0 and 1.
patch_dims	tuple[int, int, int]	Patch dimensions for the heatmap.
percentile_scale	tuple[int, int]	Scale the heatmap values to percentile using numpy.percentile().
percentile_score	bool	Use percentile score for scaling the heatmap values using scipy.stats.percentileofscore().
save_dir	path	Directory to save the heatmap images. Will be saved at {exp_base_dir}/{exp_name}/{fold-*}/{save_dir}/.
source_dir	path	Path to the directory containing the slides. Used to get predictions for the heatmap.
source_dir_annot	path	Path to the directory containing annotations corresponding to slides in source_dir. Set to null to use slides themselves for heatmaps.
use_plt	bool	Use matplotlib to generate the heatmap images. If false, heatmaps will match original slide dimensions.

model

_select

Parameter	Type	Description
classifier	str	Model to use for training and inference. Options: {CLAM_SB, EfficientNetB0, MobileNet, ResNet50, VGG16}.

model-CLAM_SB

Parameter	Type	Description
k_sample	null
dropout	null
learning_rate	null
loss_weights	dict	Keys: bag, instance
patience	null
run_eagerly	null

model-EfficientNetB0, model-MobileNet, model-ResNet50, model-VGG16

Parameter	Type	Description
freeze_ratio	null
learning_rate	null
patience	null
start_from_epoch	null

trainer

Parameter	Type	Description
batch_size	int	Batch size for training.
data_dir	path	Path to the directory containing the dataset. Should likely be dataset/{data_dir_name}/.
evaluate_only	bool	Evaluate the model on the test set only. Useful for evaluating a trained model.
exp_base_dir	path	Base directory containing all the experiment folders. Usually experiments/.
exp_name	str	Current experiment name. Will create a directory in exp_base_dir ({exp_base_dir}/{exp_name}/).
features_dir	path	Path to the directory containing the features, particularly for MIL datasets.
folds	list[int]	List of folds to be considered. Zero-indexed.
max_epochs	int	Maximum number of epochs to train the model.
overwrite_preds	bool	Overwrite predictions if already present in {exp_base_dir}/{exp_name}/{fold-*}/preds.csv.
patch_dims	tuple[int, int, int]	Patch dimensions of the dataset.
predictions_file	string	Filename of the predictions CSV file, without the extension.
save_weights_only	bool	Save only the model's weights during checkpointing. Useful for subclassed models in tf.keras.
subset_size	int	Subset size of the dataset to use for training. Use null to use the entire dataset.
use_augment	bool	Whether to use augmented dataset for training (dataset/{data_dir_name}/fold-*/{train}/).