cellmil.models.mil.standard

Classes

LitStandard(model, optimizer[, loss, ...])
MIL_fc([size_arg, dropout, n_classes, ...])	Multiple Instance Learning model with fully connected layers for binary classification.
MIL_fc_mc([size_arg, dropout, n_classes, ...])	Multiple Instance Learning model with fully connected layers for multi-class classification.

class cellmil.models.mil.standard.MIL_fc(size_arg: Union[Literal['small'], list[int]] = 'small', dropout: float = 0.0, n_classes: int = 2, top_k: int = 1, embed_dim: int = 1024)

Bases: Module

Multiple Instance Learning model with fully connected layers for binary classification.

This model processes a bag of instances, applies a feature extractor (FC layers), and performs binary classification. It selects the top k instances based on their probability scores for the positive class.

Parameters:

• size_arg – Size configuration for the network architecture (‘small’ is the only option currently).

• dropout – Dropout rate for regularization.

• n_classes – Number of classes (must be 2 for binary classification).

• top_k – Number of top instances to select based on positive class probability.

• embed_dim – Dimension of the input feature embeddings.

__init__(size_arg: Union[Literal['small'], list[int]] = 'small', dropout: float = 0.0, n_classes: int = 2, top_k: int = 1, embed_dim: int = 1024)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(h: Tensor, return_features: bool = False) → tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, dict[str, torch.Tensor]]

Forward pass of the MIL_fc model.

Parameters:

• h – Input tensor of shape [n_instances, embed_dim] containing instance embeddings.

• return_features – If True, returns the feature representations of top instances.

Returns:

• top_instance: Logits of the top instance(s).

• Y_prob: Softmax probabilities for the top instance(s).

• Y_hat: Predicted class labels for the top instance(s).

• y_probs: Softmax probabilities for all instances.

• results_dict: Additional results, may contain feature representations if return_features is True.

Return type:

A tuple containing

class cellmil.models.mil.standard.MIL_fc_mc(size_arg: Union[Literal['small'], list[int]] = 'small', dropout: float = 0.0, n_classes: int = 2, top_k: int = 1, embed_dim: int = 1024)

Bases: Module

Multiple Instance Learning model with fully connected layers for multi-class classification.

This model processes a bag of instances, applies a feature extractor (FC layers), and performs multi-class classification. It selects the top instance based on the highest probability across all classes.

Parameters:

• size_arg – Size configuration for the network architecture (‘small’ is the only option currently).

• dropout – Dropout rate for regularization.

• n_classes – Number of classes (must be > 2 for multi-class classification).

• top_k – Number of top instances to select (must be 1 for this implementation).

• embed_dim – Dimension of the input feature embeddings.

__init__(size_arg: Union[Literal['small'], list[int]] = 'small', dropout: float = 0.0, n_classes: int = 2, top_k: int = 1, embed_dim: int = 1024)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(h: Tensor, return_features: bool = False) → tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, dict[str, Any]]

Forward pass of the MIL_fc_mc model for multi-class classification.

Parameters:

• h – Input tensor of shape [n_instances, embed_dim] containing instance embeddings.

• return_features – If True, returns the feature representations of top instances.

Returns:

• top_instance: Logits of the top instance.

• Y_prob: Softmax probabilities for the top instance.

• Y_hat: Predicted class label for the top instance.

• y_probs: Softmax probabilities for all instances.

• results_dict: Additional results, may contain feature representations if return_features is True.

Return type:

A tuple containing

class cellmil.models.mil.standard.LitStandard(model: Module, optimizer: Optimizer, loss: Module = CrossEntropyLoss(), lr_scheduler: Optional[LRScheduler] = None, n_classes: int = 2)

Bases: LitGeneral

forward(x: Tensor)

Same as torch.nn.Module.forward().

Parameters:

• *args – Whatever you decide to pass into the forward method.

• **kwargs – Keyword arguments are also possible.

Returns:

Your model’s output