histox.mil

This submodule contains tools for multiple-instance learning (MIL) model training and evaluation. See Multiple-Instance Learning (MIL) for more information. A summary of the API is given below.

Training:

train_mil(): Train an MIL model, using an MIL configuration, Datasets, and a directory of bags.
build_fastai_learner(): Build and return the FastAI Learner, but do not execute training. Useful for customizing training.
build_multimodal_learner(): Build and return a FastAI Learner designed for multi-modal/multi-magnification input.

Evaluation/Inference:

eval_mil(): Evaluate an MIL model using a path to a saved model, a Dataset, and path to bags. Generates metrics.
predict_mil(): Generate predictions from an MIL model and saved bags. Returns a pandas dataframe.
predict_multimodal_mil(): Generate predictions from a multimodal MIL model. Returns a dataframe.
predict_slide(): Generate MIL predictions for a single slide. Returns a 2D array of predictions and attention.
predict_from_bags(): Low-level interface for generating predictions from a loaded MIL model and pre-loaded bag Tensors.
predict_from_multimodal_bags(): Low-level interface for generating multimodal predictions from a loaded MIL model and bag Tensors.
get_mil_tile_predictions(): Get tile-level predictions and attention from a saved MIL model for a given Dataset and saved bags.
generate_attention_heatmaps(): Generate and save attention heatmaps.
generate_mil_features(): Get last-layer activations from an MIL model. Returns an MILFeatures object.

Main functions

histox.mil.mil_config(model: str | Callable, trainer: str = 'fastai', **kwargs)[source]

Create a multiple-instance learning (MIL) training configuration.

All models by default are trained with the FastAI trainer. Additional trainers and additional models can be installed with histox-extras.

Parameters:

model (str, Callable) – Either the name of a model, or a custom torch module. Valid model names include "attention_mil", "transmil", and "bistro.transformer".
trainer (str) – Type of MIL trainer to use. Only ‘fastai’ is available, unless additional trainers are installed.
**kwargs – All additional keyword arguments are passed to histox.mil.TrainerConfig

histox.mil.train_mil(config: TrainerConfig, train_dataset: Dataset, val_dataset: Dataset | None, outcomes: str | List[str], bags: str | List[str], *, outdir: str = 'mil', exp_label: str | None = None, **kwargs) → Learner[source]

Train a multiple-instance learning (MIL) model.

This high-level trainer facilitates training from a given MIL configuration, using Datasets as input and with input features taken from a given directory of bags.

Parameters:

config (histox.mil.TrainerConfig) – Trainer and model configuration.
train_dataset (histox.Dataset) – Training dataset.
val_dataset (histox.Dataset) – Validation dataset.
outcomes (str) – Outcome column (annotation header) from which to derive category labels.
bags (str) – Either a path to directory with *.pt files, or a list of paths to individual *.pt files. Each file should contain exported feature vectors, with each file containing all tile features for one patient.

Keyword Arguments:

outdir (str) – Directory in which to save model and results.
exp_label (str) – Experiment label, used for naming the subdirectory in the {project root}/mil folder, where training history and the model will be saved.
attention_heatmaps (bool) – Generate attention heatmaps for slides. Not available for multi-modal MIL models. Defaults to False.
interpolation (str, optional) – Interpolation strategy for smoothing attention heatmaps. Defaults to ‘bicubic’.
cmap (str, optional) – Matplotlib colormap for heatmap. Can be any valid matplotlib colormap. Defaults to ‘inferno’.
norm (str, optional) – Normalization strategy for assigning heatmap values to colors. Either ‘two_slope’, or any other valid value for the norm argument of matplotlib.pyplot.imshow. If ‘two_slope’, normalizes values less than 0 and greater than 0 separately. Defaults to None.

histox.mil.build_fastai_learner(config: TrainerConfig, train_dataset: Dataset, val_dataset: Dataset, outcomes: str | List[str], bags: str | ndarray | List[str], *, outdir: str = 'mil', return_shape: bool = False, **kwargs) → Learner[source]

Build a FastAI Learner for training an MIL model.

Does not execute training. Useful for customizing a Learner object prior to training.

Parameters:

train_dataset (histox.Dataset) – Training dataset.
val_dataset (histox.Dataset) – Validation dataset.
outcomes (str) – Outcome column (annotation header) from which to derive category labels.
bags (str) – list of paths to individual *.pt files. Each file should contain exported feature vectors, with each file containing all tile features for one patient.

Keyword Arguments:

outdir (str) – Directory in which to save model and results.
return_shape (bool) – Return the input and output shapes of the model. Defaults to False.
exp_label (str) – Experiment label, used for naming the subdirectory in the outdir folder, where training history and the model will be saved.
lr (float) – Learning rate, or maximum learning rate if fit_one_cycle=True.
epochs (int) – Maximum epochs.
**kwargs – Additional keyword arguments to pass to the FastAI learner.

Returns:

fastai.learner.Learner, and optionally a tuple of input and output shapes if return_shape=True.

histox.mil.build_multimodal_learner(config: TrainerConfig, train_dataset: Dataset, val_dataset: Dataset, outcomes: str | List[str], bags: ndarray | List[str], *, outdir: str = 'mil', return_shape: bool = False) → Learner[source]

Build a multi-magnification FastAI Learner for training an MIL model.

Does not execute training. Useful for customizing a Learner object prior to training.

Parameters:

train_dataset (histox.Dataset) – Training dataset.
val_dataset (histox.Dataset) – Validation dataset.
outcomes (str) – Outcome column (annotation header) from which to derive category labels.
bags (list(str)) – List of bag directories containing *.pt files, one directory for each mode.

Keyword Arguments:

outdir (str) – Directory in which to save model and results.
return_shape (bool) – Return the input and output shapes of the model. Defaults to False.
exp_label (str) – Experiment label, used for naming the subdirectory in the outdir folder, where training history and the model will be saved.
lr (float) – Learning rate, or maximum learning rate if fit_one_cycle=True.
epochs (int) – Maximum epochs.
**kwargs – Additional keyword arguments to pass to the FastAI learner.

Returns:

fastai.learner.Learner, and optionally a tuple of input and output shapes if return_shape=True.

histox.mil.eval_mil(weights: str, dataset: Dataset, outcomes: str | List[str], bags: str | List[str], config: TrainerConfig | None = None, *, outdir: str = 'mil', attention_heatmaps: bool = False, uq: bool = False, aggregation_level: str | None = None, **heatmap_kwargs) → DataFrame[source]

Evaluate a multiple-instance learning model.

Saves results for the evaluation in the target folder, including predictions (parquet format), attention (Numpy format for each slide), and attention heatmaps (if attention_heatmaps=True).

Logs classifier metrics (AUROC and AP) to the console.

Parameters:

weights (str) – Path to model weights to load.
dataset (hx.Dataset) – Dataset to evaluation.
outcomes (str, list(str)) – Outcomes.
bags (str, list(str)) – Path to bags, or list of bag file paths. Each bag should contain PyTorch array of features from all tiles in a slide, with the shape (n_tiles, n_features).
config (histox.mil.TrainerConfig) – Configuration for building model. If weights is a path to a model directory, will attempt to read mil_params.json from this location and load saved configuration. Defaults to None.

Keyword Arguments:

outdir (str) – Path at which to save results.
attention_heatmaps (bool) – Generate attention heatmaps for slides. Not available for multi-modal MIL models. Defaults to False.
interpolation (str, optional) – Interpolation strategy for smoothing attention heatmaps. Defaults to ‘bicubic’.
aggregation_level (str, optional) – Aggregation level for predictions. Either ‘slide’ or ‘patient’. Defaults to None (uses the model configuration).
cmap (str, optional) – Matplotlib colormap for heatmap. Can be any valid matplotlib colormap. Defaults to ‘inferno’.
norm (str, optional) – Normalization strategy for assigning heatmap values to colors. Either ‘two_slope’, or any other valid value for the norm argument of matplotlib.pyplot.imshow. If ‘two_slope’, normalizes values less than 0 and greater than 0 separately. Defaults to None.

Generate predictions for a dataset from a saved MIL model.

Parameters:

model (torch.nn.Module) – Model from which to generate predictions.
dataset (hx.Dataset) – Dataset from which to generation predictions.
outcomes (str, list(str)) – Outcomes.
bags (str, list(str)) – Path to bags, or list of bag file paths. Each bag should contain PyTorch array of features from all tiles in a slide, with the shape (n_tiles, n_features).

Keyword Arguments:

config (histox.mil.TrainerConfig) – Configuration for the MIL model. Required if model is a loaded torch.nn.Module. Defaults to None.
attention (bool) – Whether to calculate attention scores. Defaults to False.
uq (bool) – Whether to generate uncertainty estimates. Experimental. Defaults to False.
aggregation_level (str) – Aggregation level for predictions. Either ‘slide’ or ‘patient’. Defaults to None.
attention_pooling (str) – Attention pooling strategy. Either ‘avg’ or ‘max’. Defaults to None.

Returns:

Dataframe of predictions.

list(np.ndarray): Attention scores (if attention=True)

Return type:

pd.DataFrame

histox.mil.predict_multimodal_mil(model: str | Callable, dataset: Dataset, outcomes: str | List[str], bags: ndarray | List[List[str]], *, config: TrainerConfig | None = None, attention: bool = False, aggregation_level: str | None = None, **kwargs) → DataFrame | Tuple[DataFrame, List[ndarray]][source]

Generate predictions for a dataset from a saved multimodal MIL model.

Parameters:

model (torch.nn.Module) – Model from which to generate predictions.
dataset (hx.Dataset) – Dataset from which to generation predictions.
outcomes (str, list(str)) – Outcomes.
bags (str, list(str)) – Path to bags, or list of bag file paths. Each bag should contain PyTorch array of features from all tiles in a slide, with the shape (n_tiles, n_features).

Keyword Arguments:

config (histox.mil.TrainerConfig) – Configuration for the MIL model. Required if model is a loaded torch.nn.Module. Defaults to None.
attention (bool) – Whether to calculate attention scores. Defaults to False.
uq (bool) – Whether to generate uncertainty estimates. Defaults to False.
aggregation_level (str) – Aggregation level for predictions. Either ‘slide’ or ‘patient’. Defaults to None.
attention_pooling (str) – Attention pooling strategy. Either ‘avg’ or ‘max’. Defaults to None.

Returns:

Dataframe of predictions.

list(np.ndarray): Attention scores (if attention=True)

Return type:

pd.DataFrame

histox.mil.predict_from_bags(model: torch.nn.Module, bags: ndarray | List[str], *, attention: bool = False, attention_pooling: str | None = None, use_lens: bool = False, device: Any | None = None, apply_softmax: bool | None = None, uq: bool = False) → Tuple[ndarray, List[ndarray]][source]

Generate MIL predictions for a list of bags.

Predictions are generated for each bag in the list one at a time, and not batched.

Parameters:

model (torch.nn.Module) – Loaded PyTorch MIL model.
bags (np.ndarray, list(str)) – Bags to generate predictions for. Each bag should contain PyTorch array of features from all tiles in a slide, with the shape (n_tiles, n_features).

Keyword Arguments:

attention (bool) – Whether to calculate attention scores. Defaults to False.
attention_pooling (str, optional) – Pooling strategy for attention scores. Can be ‘avg’, ‘max’, or None. Defaults to None.
use_lens (bool) – Whether to use the length of each bag as an additional input to the model. Defaults to False.
device (str, optional) – Device on which to run inference. Defaults to None.
apply_softmax (bool) – Whether to apply softmax to the model output. Defaults to True for categorical outcomes, False for continuous outcomes.
uq (bool) – Whether to generate uncertainty estimates. Defaults to False.

Returns:

Predictions and attention scores.

Return type:

Tuple[np.ndarray, List[np.ndarray]]

histox.mil.predict_from_multimodal_bags(model: torch.nn.Module, bags: List[ndarray] | List[List[str]], *, attention: bool = True, attention_pooling: str | None = None, use_lens: bool = True, device: Any | None = None, apply_softmax: bool | None = None) → Tuple[ndarray, List[List[ndarray]]][source]

Generate multi-mag MIL predictions for a nested list of bags.

Parameters:

model (torch.nn.Module) – Loaded PyTorch MIL model.
bags (list(list(str))) – Nested list of bags to generate predictions for. Each bag should contain PyTorch array of features from all tiles in a slide, with the shape (n_tiles, n_features).

Keyword Arguments:

attention (bool) – Whether to calculate attention scores. Defaults to False.
attention_pooling (str, optional) – Pooling strategy for attention scores. Can be ‘avg’, ‘max’, or None. Defaults to None.
use_lens (bool) – Whether to use the length of each bag as an additional input to the model. Defaults to False.
device (str, optional) – Device on which to run inference. Defaults to None.
apply_softmax (bool) – Whether to apply softmax to the model output. Defaults to True for categorical outcomes, False for continuous

Returns:

Predictions and attention scores.

Return type:

Tuple[np.ndarray, List[List[np.ndarray]]]

histox.mil.predict_slide(model: str, slide: str | WSI, extractor: BaseFeatureExtractor | None = None, *, normalizer: StainNormalizer | None = None, config: TrainerConfig | None = None, attention: bool = False, native_normalizer: bool | None = True, extractor_kwargs: dict | None = None, **kwargs) → Tuple[ndarray, ndarray | None][source]

Generate predictions (and attention) for a single slide.

Parameters:

model (str) – Path to MIL model.
slide (str) – Path to slide.
extractor (histox.mil.BaseFeatureExtractor, optional) –
Feature extractor. If not provided, will attempt to auto-detect extractor from model.

Note

If the extractor has a stain normalizer, this will be used to normalize the slide before extracting features.

Keyword Arguments:

normalizer (histox.stain.StainNormalizer, optional) – Stain normalizer. If not provided, will attempt to use stain normalizer from extractor.
config (histox.mil.TrainerConfig) – Configuration for building model. If None, will attempt to read mil_params.json from the model directory and load saved configuration. Defaults to None.
attention (bool) – Whether to return attention scores. Defaults to False.
attention_pooling (str) – Attention pooling strategy. Either ‘avg’ or ‘max’. Defaults to None.
native_normalizer (bool, optional) – Whether to use PyTorch/Tensorflow-native stain normalization, if applicable. If False, will use the OpenCV/Numpy implementations. Defaults to None, which auto-detects based on the slide backend (False if libvips, True if cucim). This behavior is due to performance issued when using native stain normalization with libvips-compatible multiprocessing.

Returns:

Predictions and attention scores. Attention scores are None if attention is False. For single-channel attention, this is a masked 2D array with the same shape as the slide grid (arranged as a heatmap, with unused tiles masked). For multi-channel attention, this is a masked 3D array with shape (n_channels, X, Y).

Return type:

Tuple[np.ndarray, Optional[np.ndarray]]

Generate tile-level predictions for a MIL model.

Parameters:

weights (str) – Path to model weights to load.
dataset (histox.Dataset) – Dataset.
bags (str, list(str)) – Path to bags, or list of bag file paths. Each bag should contain PyTorch array of features from all tiles in a slide, with the shape (n_tiles, n_features).

Keyword Arguments:

config (histox.mil.TrainerConfig) – Configuration for building model. If weights is a path to a model directory, will attempt to read mil_params.json from this location and load saved configuration. Defaults to None.
outcomes (str, list(str)) – Outcomes.
dest (str) – Path at which to save tile predictions.
uq (bool) – Whether to generate uncertainty estimates. Defaults to False.
device (str, optional) – Device on which to run inference. Defaults to None.
tile_batch_size (int) – Batch size for tile-level predictions. Defaults to 512.
attention_pooling (str) – Attention pooling strategy. Either ‘avg’ or ‘max’. Defaults to None.

Returns:

Dataframe of tile predictions.

Return type:

pd.DataFrame

histox.mil.generate_attention_heatmaps(outdir: str, dataset: Dataset, bags: List[str] | ndarray, attention: ndarray | List[ndarray], **kwargs) → None[source]

Generate and save attention heatmaps for a dataset.

Parameters:

outdir (str) – Path at which to save heatmap images.
dataset (hx.Dataset) – Dataset.
bags (str, list(str)) – List of bag file paths. Each bag should contain PyTorch array of features from all tiles in a slide, with the shape (n_tiles, n_features).
attention (list(np.ndarray)) – Attention scores for each slide. Length of attention should equal the length of bags.

Keyword Arguments:

interpolation (str, optional) – Interpolation strategy for smoothing heatmap. Defaults to ‘bicubic’.
cmap (str, optional) – Matplotlib colormap for heatmap. Can be any valid matplotlib colormap. Defaults to ‘inferno’.
norm (str, optional) – Normalization strategy for assigning heatmap values to colors. Either ‘two_slope’, or any other valid value for the norm argument of matplotlib.pyplot.imshow. If ‘two_slope’, normalizes values less than 0 and greater than 0 separately. Defaults to None.

histox.mil.generate_mil_features(weights: str, dataset: hx.Dataset, bags: str | ndarray | List[str], *, config: TrainerConfig | None = None) → MILFeatures[source]

Generate activations weights from the last layer of an MIL model.

Returns MILFeatures object.

Parameters:

weights (str) – Path to model weights to load.
config (histox.mil.TrainerConfig) – Configuration for building model. If weights is a path to a model directory, will attempt to read mil_params.json from this location and load saved configuration. Defaults to None.
dataset (histox.Dataset) – Dataset.
outcomes (str, list(str)) – Outcomes.
bags (str, list(str)) – Path to bags, or list of bag file paths. Each bag should contain PyTorch array of features from all tiles in a slide, with the shape (n_tiles, n_features).

TrainerConfig

mil.TrainerConfig: alias of <MagicMock name=’mock.TrainerConfig’ id=’129895486449616’>

`TrainerConfig.model_fn`	MIL model architecture (class/module).
`TrainerConfig.loss_fn`	MIL loss function.
`TrainerConfig.is_multimodal`	Whether the model is multimodal.
`TrainerConfig.model_type`	Type of model (classification or regression).

mil.TrainerConfig.to_dict(*args, **kwargs)

MagicMock is a subclass of Mock with default implementations of most of the magic methods. You can use MagicMock without having to configure the magic methods yourself.

If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.TrainerConfig.json_dump(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.TrainerConfig.is_classification(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.TrainerConfig.get_metrics(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.TrainerConfig.prepare_training(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.TrainerConfig.build_model(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.TrainerConfig.predict(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.TrainerConfig.batched_predict(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.TrainerConfig.train(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.TrainerConfig.eval(*args, **kwargs)

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Attributes and the return value of a MagicMock will also be MagicMocks.

mil.TrainerConfig.build_train_dataloader(*args, **kwargs)

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mil.TrainerConfig.build_val_dataloader(*args, **kwargs)

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mil.TrainerConfig.inspect_batch(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.TrainerConfig.run_metrics(*args, **kwargs)

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Attributes and the return value of a MagicMock will also be MagicMocks.

MILModelConfig

class histox.mil.MILModelConfig(model: str | Callable = 'attention_mil', *, use_lens: bool | None = None, apply_softmax: bool = True, model_kwargs: dict | None = None, validate: bool = True, loss: str | Callable = 'cross_entropy', **kwargs)[source]

`MILModelConfig.apply_softmax`	Whether softmax will be applied to model outputs.
`MILModelConfig.loss_fn`	MIL loss function.
`MILModelConfig.model_fn`	MIL model architecture (class/module).
`MILModelConfig.model_type`	Type of model (classification or regression).
`MILModelConfig.is_multimodal`	Whether the model is multimodal.

mil.MILModelConfig.is_classification(*args, **kwargs)

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Attributes and the return value of a MagicMock will also be MagicMocks.

mil.MILModelConfig.to_dict(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.MILModelConfig.inspect_batch(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.MILModelConfig.build_model(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.MILModelConfig.predict(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.MILModelConfig.batched_predict(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

mil.MILModelConfig.run_metrics(*args, **kwargs)

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If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

Attributes and the return value of a MagicMock will also be MagicMocks.

CLAMModelConfig

The CLAM model configuration class requires slideflow-gpl, which can be installed with:

pip install slideflow-gpl

Once installed, the class is available at histox.clam.CLAMModelConfig.

clam.CLAMModelConfig: alias of <MagicMock name=’mock.CLAMModelConfig’ id=’129895485921776’>