Source code for monai.handlers.mean_dice
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from __future__ import annotations
from collections.abc import Callable
from monai.handlers.ignite_metric import IgniteMetricHandler
from monai.metrics import DiceMetric
from monai.utils import MetricReduction
[docs]
class MeanDice(IgniteMetricHandler):
"""
Computes Dice score metric from full size Tensor and collects average over batch, class-channels, iterations.
"""
[docs]
def __init__(
self,
include_background: bool = True,
reduction: MetricReduction | str = MetricReduction.MEAN,
num_classes: int | None = None,
output_transform: Callable = lambda x: x,
save_details: bool = True,
return_with_label: bool | list[str] = False,
) -> None:
"""
Args:
include_background: whether to include dice computation on the first channel of the predicted output.
Defaults to True.
reduction: define the mode to reduce metrics, will only execute reduction on `not-nan` values,
available reduction modes: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, default to ``"mean"``. if "none", will not do reduction.
num_classes: number of input channels (always including the background). When this is None,
``y_pred.shape[1]`` will be used. This option is useful when both ``y_pred`` and ``y`` are
single-channel class indices and the number of classes is not automatically inferred from data.
output_transform: callable to extract `y_pred` and `y` from `ignite.engine.state.output` then
construct `(y_pred, y)` pair, where `y_pred` and `y` can be `batch-first` Tensors or
lists of `channel-first` Tensors. the form of `(y_pred, y)` is required by the `update()`.
`engine.state` and `output_transform` inherit from the ignite concept:
https://pytorch.org/ignite/concepts.html#state, explanation and usage example are in the tutorial:
https://github.com/Project-MONAI/tutorials/blob/master/modules/batch_output_transform.ipynb.
save_details: whether to save metric computation details per image, for example: mean dice of every image.
default to True, will save to `engine.state.metric_details` dict with the metric name as key.
return_with_label: whether to return the metrics with label, only works when reduction is "mean_batch".
If `True`, use "label_{index}" as the key corresponding to C channels; if 'include_background' is True,
the index begins at "0", otherwise at "1". It can also take a list of label names.
The outcome will then be returned as a dictionary.
See also:
:py:meth:`monai.metrics.meandice.compute_dice`
"""
metric_fn = DiceMetric(
include_background=include_background,
reduction=reduction,
num_classes=num_classes,
return_with_label=return_with_label,
)
super().__init__(metric_fn=metric_fn, output_transform=output_transform, save_details=save_details)