Metrics

Mean Dice

monai.metrics.compute_meandice(y_pred, y, include_background=True, to_onehot_y=False, mutually_exclusive=False, sigmoid=False, logit_thresh=0.5)[source]

Computes Dice score metric from full size Tensor and collects average.

Parameters

  • y_pred (torch.Tensor) – input data to compute, typical segmentation model output. it must be one-hot format and first dim is batch, example shape: [16, 3, 32, 32].

  • y (torch.Tensor) – ground truth to compute mean dice metric, the first dim is batch. example shape: [16, 1, 32, 32] will be converted into [16, 3, 32, 32]. alternative shape: [16, 3, 32, 32] and set to_onehot_y=False to use 3-class labels directly.

  • include_background (bool) – whether to skip Dice computation on the first channel of the predicted output. Defaults to True.

  • to_onehot_y (bool) – whether to convert y into the one-hot format. Defaults to False.

  • mutually_exclusive (bool) – if True, y_pred will be converted into a binary matrix using a combination of argmax and to_onehot. Defaults to False.

  • sigmoid (bool) – whether to add sigmoid function to y_pred before computation. Defaults to False.

  • logit_thresh (float) – the threshold value used to convert (after sigmoid if sigmoid=True) y_pred into a binary matrix. Defaults to 0.5.

Returns

Dice scores per batch and per class, (shape [batch_size, n_classes]).

Raises

ValueError – sigmoid=True is incompatible with mutually_exclusive=True.

Note

This method provides two options to convert y_pred into a binary matrix

  1. when mutually_exclusive is True, it uses a combination of argmax and to_onehot,

  2. when mutually_exclusive is False, it uses a threshold logit_thresh (optionally with a sigmoid function before thresholding).

class monai.metrics.DiceMetric(include_background=True, to_onehot_y=False, mutually_exclusive=False, sigmoid=False, logit_thresh=0.5, reduction=<MetricReduction.MEAN: 'mean'>)[source]

Compute average Dice loss between two tensors. It can support both multi-classes and multi-labels tasks. Input logits y_pred (BNHW[D] where N is number of classes) is compared with ground truth y (BNHW[D]). Axis N of y_preds is expected to have logit predictions for each class rather than being image channels, while the same axis of y can be 1 or N (one-hot format). The include_background class attribute can be set to False for an instance of DiceLoss to exclude the first category (channel index 0) which is by convention assumed to be background. If the non-background segmentations are small compared to the total image size they can get overwhelmed by the signal from the background so excluding it in such cases helps convergence.

Parameters

  • include_background (bool) – whether to skip Dice computation on the first channel of the predicted output. Defaults to True.

  • to_onehot_y (bool) – whether to convert y into the one-hot format. Defaults to False.

  • mutually_exclusive (bool) – if True, y_pred will be converted into a binary matrix using a combination of argmax and to_onehot. Defaults to False.

  • sigmoid (bool) – whether to add sigmoid function to y_pred before computation. Defaults to False.

  • logit_thresh (float) – the threshold value used to convert (after sigmoid if sigmoid=True) y_pred into a binary matrix. Defaults to 0.5.

  • reduction (Union[MetricReduction, str]) – {"none", "mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel"} Define the mode to reduce computation result of 1 batch data. Defaults to "mean".

Area under the ROC curve

monai.metrics.compute_roc_auc(y_pred, y, to_onehot_y=False, softmax=False, average=<Average.MACRO: 'macro'>)[source]

Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC). Referring to: sklearn.metrics.roc_auc_score.

Parameters

  • y_pred (torch.Tensor) – input data to compute, typical classification model output. it must be One-Hot format and first dim is batch, example shape: [16] or [16, 2].

  • y (torch.Tensor) – ground truth to compute ROC AUC metric, the first dim is batch. example shape: [16, 1] will be converted into [16, 2] (where 2 is inferred from y_pred).

  • to_onehot_y (bool) – whether to convert y into the one-hot format. Defaults to False.

  • softmax (bool) – whether to add softmax function to y_pred before computation. Defaults to False.

  • average (Union[Average, str]) –

    {"macro", "weighted", "micro", "none"} Type of averaging performed if not binary classification. Defaults to "macro".

    • "macro": calculate metrics for each label, and find their unweighted mean.

      This does not take label imbalance into account.

    • "weighted": calculate metrics for each label, and find their average,

      weighted by support (the number of true instances for each label).

    • "micro": calculate metrics globally by considering each element of the label

      indicator matrix as a label.

    • "none": the scores for each class are returned.

Raises

  • ValueError – predictions should be of shape (batch_size, n_classes) or (batch_size, ).

  • ValueError – targets should be of shape (batch_size, n_classes) or (batch_size, ).

  • ValueError – unsupported average method.

Note

ROCAUC expects y to be comprised of 0’s and 1’s. y_pred must be either prob. estimates or confidence values.