revise MILModel docstring
@@ -11,23 +11,24 @@ models, _ = optional_import("torchvision.models")
class MILModel(nn.Module):
"""
Multiple Instance Learning (MIL) model, with a backbone classification model.
- Currently, it only works for 2D images, typical use case is for classification of the
+ Currently, it only works for 2D images, a typical use case is for classification of the
digital pathology whole slide images. The expected shape of input data is `[B, N, C, H, W]`,
- where `B` is the batch_size of PyTorch Dataloader and `N` is the number of the instances
+ where `B` is the batch_size of PyTorch Dataloader and `N` is the number of instances
extracted from every original image in the batch. A tutorial example is available at:
https://github.com/Project-MONAI/tutorials/tree/master/pathology/multiple_instance_learning.
Args:
num_classes: number of output classes.
- mil_mode: MIL algorithm, available values:
- "mean" - average features from all instances, equivalent to pure CNN (non MIL).
- "max - retain only the instance with the max probability for loss calculation.
- "att" - attention based MIL https://arxiv.org/abs/1802.04712.
- "att_trans" - transformer MIL https://arxiv.org/abs/2111.01556.
- "att_trans_pyramid" - transformer pyramid MIL https://arxiv.org/abs/2111.01556.
- Defaults to ``att``.
+ mil_mode: MIL algorithm, available values (Defaults to ``"att"``):
+
+ - ``"mean"`` - average features from all instances, equivalent to pure CNN (non MIL).
+ - ``"max"`` - retain only the instance with the max probability for loss calculation.
+ - ``"att"`` - attention based MIL https://arxiv.org/abs/1802.04712.
+ - ``"att_trans"`` - transformer MIL https://arxiv.org/abs/2111.01556.
+ - ``"att_trans_pyramid"`` - transformer pyramid MIL https://arxiv.org/abs/2111.01556.
+
pretrained: init backbone with pretrained weights, defaults to ``True``.
- backbone: Backbone classifier CNN (either None, nn.Module that returns features,
+ backbone: Backbone classifier CNN (either ``None``, a ``nn.Module`` that returns features,
or a string name of a torchvision model).
Defaults to ``None``, in which case ResNet50 is used.
backbone_num_features: Number of output features of the backbone CNN
