pathology-streamingclam

Lightstream implementation of the StreamingCLAM model

Requirements

The StreamingCLAM package requires the following dependencies:

• Python3.10 or higher
• lightstream
• albumentationsxl
• pytorch 2.0.0 or higher
• pytorch lightning 2.0.
• torchvision

Running StreamingCLAM

To run the streamingclam, first git clone the repository, and optionally add it to your PYTHONPATH if needed:

git clone [email protected]:DIAGNijmegen/pathology-streamingclam.git

Then, run the main.py with at least the required options. For example, to train the streamingCLAM model starting from ImageNet weights:

python main.py --image_path <path_to_images> --train_csv <path_to_csv_file> --val_csv <path_to_val_csv> --default_save_dir <path_to_save_results>

Or, if you have tissue masks, you can use them by passing them into main.py as such:

python main.py --image_path <path_to_images> --mask_path <path_to_tissue_masks> --mask_suffix <mask_suffix> --train_csv <path_to_csv_file> --val_csv <path_to_val_csv> --default_save_dir <path_to_save_results>

We assume that the masks have the same names as the images, but can have an additional postfix, e.g. if the name of the image is tumor_001.tif, then the mask can be called tumor_001_tissue.tif, and you must add mask_suffix _tissue as an additional argument.

Structure of the csv_files

The structure of the csv files must be as follows. A concrete example is posted under the data_splits folder. Column headers are not required. However, it is important that the first column has the image names, and the second column contains the labels as integer numbers.

slide_id,label
TRAIN_1,1
TRAIN_2,1
TRAIN_3,0

Options

There are quite some options (disable boolean options by prepending with no_, so e.g., no_use_augmentations):

Required options	Description
image_path: str	The name of the current experiment, used for saving checkpoints.
default_save_dir: str	The directory where logs and checkpoints are stored
mask_path: int	The number of classes in the task.
train_csv: str	The filenames (without extension) and labels of train set.
val_csv: str	The filenames (without extension) and labels of validation or test set.
test_csv: str	The directory where the images reside.
mask_suffix: str = "_tissue:"	the suffix for mask tissues e.g. tumor_069_<mask_suffix>.tif. Default is "_tissue"
mode: str = "fit"	fit, validation, test or predict, default is fit
unfreeze_streaming_layers_at_epoch: int = 15	The epoch to unfreeze and train the entire network. Default is 20
Trainer options
num_epochs: int = 50	The maximum number of epochs to train, default is 50
strategy: str = "ddp_find_unused_parameters_true"	The training strategy. Suggested to use 'auto' for 1 gpu, and "ddp_find_unused_parameters_true" for multiple gpu's.
ckp_path: str = ""	# the name fo the ckp file within the default_save_dir
resume: bool = True	# Whether to resume training from the last/best epoch
grad_batches: int = 2	# Gradient accumulation: the amount of batches before optimizer step. Default is 2
num_gpus: int	The number of devices to use
precision: str = "16-mixed"	The precision during training. Recommended to use "16-mixed", "16-true", "bf16-mixed", "bf16-true"
StreamingCLAM options
encoder: str = "resnet34"	"resnet18", "resnet34", "resnet50". Default is "resnet34"
branch: str = "sb"	single branch "sb" or multi-branch "mb" clam head. Default is "sb"
max_pool_kernel: int = 8	The size of the max pool kernel and stride at the end of the resnet backbone
num_classes: int = 2
loss_fn: torch.nn.Module = torch.nn.CrossEntropyLoss()	The loss function for the CLAM classification head. Default is torch.nn.CrossEntropyLoss()
instance_eval: bool = False	Whether to use CLAM's instance_eval. Default is "False"
return_features: bool = False	Return the features "M" of the CLAM attention "M = torch.mm(A, h)" where A is the normalized attention map and h is the input features of the resnet
attention_only: bool = False	Whether to only return the attention map A_raw. In this case, the attention is unnormalized (no softmax)
stream_max_pool_kernel: bool = False	If set to true, will incorporate the max_pool_kernel into the streaming network. This will save memory, at the cost of speed
learning_rate: float = 2e-4	The learning rate for training the CLAM head
Streaming options
tile_size: int = 9984	The tile size for the streaming network. Higher uses more GPU memory, but also speeds up computations
statistics_on_cpu: bool = True	Calculate the streaming network statistics on the cpu. Default is True
verbose: bool = True	Verbose streaming network output. Default is True
train_streaming_layers: bool = False	Whether to train the streaming layers. Default is False, will set to true at "unfreeze_streaming_layers_at_epoc"
normalize_on_gpu: bool = True	Do not change this!: Normalize with imagenet weights on the gpu. Default is True
copy_to_gpu: bool = False	Copies the entire image to the gpu. Recommended False if image > 16000x16000
Dataloader options
image_size: int = 65536	represents image size if variable_input_shape=False, else the maximum image size
variable_input_shapes: bool = True	If true, will not pad/crop images to a uniform size specified by image_size. Can save RAM and speed up training/inference
filetype: str = ".tif"	The filetype of the images. Default is ".tif"
read_level: int = 1	The level of the tif file (0 is highest resolution)
num_workers: int = 3	The amount of workers per gpu. Default is 3.
use_augmentations: bool = True	Whether to use augmentations specified in the file data.dataset.py. Default is True