Metrics¶
FROC¶

monai.metrics.
compute_froc_score
(fps_per_image, total_sensitivity, eval_thresholds=(0.25, 0.5, 1, 2, 4, 8))[source]¶ This function is modified from the official evaluation code of CAMELYON 16 Challenge, and used to compute the challenge’s second evaluation metric, which is defined as the average sensitivity at the predefined false positive rates per whole slide image.
 Parameters
fps_per_image (
ndarray
) – the average number of false positives per image for different thresholds.total_sensitivity (
ndarray
) – sensitivities (true positive rates) for different thresholds.eval_thresholds (
Tuple
) – the false positive rates for calculating the average sensitivity. Defaults to (0.25, 0.5, 1, 2, 4, 8) which is the same as the CAMELYON 16 Challenge.
Mean Dice¶

monai.metrics.
compute_meandice
(y_pred, y, include_background=True)[source]¶ Computes Dice score metric from full size Tensor and collects average.
 Parameters
y_pred (
Tensor
) – input data to compute, typical segmentation model output. It must be onehot format and first dim is batch, example shape: [16, 3, 32, 32]. The values should be binarized.y (
Tensor
) – ground truth to compute mean dice metric. It must be onehot format and first dim is batch. The values should be binarized.include_background (
bool
) – whether to skip Dice computation on the first channel of the predicted output. Defaults to True.
 Return type
Tensor
 Returns
Dice scores per batch and per class, (shape [batch_size, n_classes]).
 Raises
ValueError – when y_pred and y have different shapes.

class
monai.metrics.
DiceMetric
(include_background=True, reduction=<MetricReduction.MEAN: 'mean'>)[source]¶ Compute average Dice loss between two tensors. It can support both multiclasses and multilabels tasks. Input y_pred (BNHW[D] where N is number of classes) is compared with ground truth y (BNHW[D]). y_preds is expected to have binarized predictions and y should be in onehot format. You can use suitable transforms in
monai.transforms.post
first to achieve binarized values. The include_background parameter can be set toFalse
for an instance of DiceLoss to exclude the first category (channel index 0) which is by convention assumed to be background. If the nonbackground 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 toTrue
.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, 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 (
Tensor
) – input data to compute, typical classification model output. it must be OneHot format and first dim is batch, example shape: [16] or [16, 2].y (
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).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 labelindicator matrix as a label.
"none"
: the scores for each class are returned.
 Raises
ValueError – When
y_pred
dimension is not one of [1, 2].ValueError – When
y
dimension is not one of [1, 2].ValueError – When
average
is not one of [“macro”, “weighted”, “micro”, “none”].
Note
ROCAUC expects y to be comprised of 0’s and 1’s. y_pred must be either prob. estimates or confidence values.
Confusion matrix¶

monai.metrics.
get_confusion_matrix
(y_pred, y, include_background=True)[source]¶ Compute confusion matrix. A tensor with the shape [BC4] will be returned. Where, the third dimension represents the number of true positive, false positive, true negative and false negative values for each channel of each sample within the input batch. Where, B equals to the batch size and C equals to the number of classes that need to be computed.
 Parameters
y_pred (
Tensor
) – input data to compute. It must be onehot format and first dim is batch. The values should be binarized.y (
Tensor
) – ground truth to compute the metric. It must be onehot format and first dim is batch. The values should be binarized.include_background (
bool
) – whether to skip metric computation on the first channel of the predicted output. Defaults to True.
 Raises
ValueError – when y_pred and y have different shapes.

class
monai.metrics.
ConfusionMatrixMetric
(include_background=True, metric_name='hit_rate', compute_sample=False, reduction=<MetricReduction.MEAN: 'mean'>)[source]¶ Compute confusion matrix related metrics. This function supports to calculate all metrics mentioned in: Confusion matrix. It can support both multiclasses and multilabels classification and segmentation tasks. y_preds is expected to have binarized predictions and y should be in onehot format. You can use suitable transforms in
monai.transforms.post
first to achieve binarized values. The include_background parameter can be set toFalse
for an instance to exclude the first category (channel index 0) which is by convention assumed to be background. If the nonbackground 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 metric computation on the first channel of the predicted output. Defaults to True.metric_name (
Union
[Sequence
[str
],str
]) – ["sensitivity"
,"specificity"
,"precision"
,"negative predictive value"
,"miss rate"
,"fall out"
,"false discovery rate"
,"false omission rate"
,"prevalence threshold"
,"threat score"
,"accuracy"
,"balanced accuracy"
,"f1 score"
,"matthews correlation coefficient"
,"fowlkes mallows index"
,"informedness"
,"markedness"
] Some of the metrics have multiple aliases (as shown in the wikipedia page aforementioned), and you can also input those names instead. Except for input only one metric, multiple metrics are also supported via input a sequence of metric names, such as (“sensitivity”, “precision”, “recall”), ifcompute_sample
isTrue
, multiplef
andnot_nans
will be returned with the same order as input names when calling the class.compute_sample (
bool
) – ifTrue
, each sample’s metric will be computed first. IfFalse
, the confusion matrix for each image (the output of functionget_confusion_matrix
) will be returned. In this way, users should achieve the confusion matrixes for all images during an epoch and then usecompute_confusion_matrix_metric
to calculate the metric. Defaults toFalse
.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. Reduction will only be employed whencompute_sample
isTrue
. Defaults to"mean"
.
Hausdorff distance¶

monai.metrics.
compute_hausdorff_distance
(y_pred, y, include_background=False, distance_metric='euclidean', percentile=None, directed=False)[source]¶ Compute the Hausdorff distance.
 Parameters
y_pred (
Union
[ndarray
,Tensor
]) – input data to compute, typical segmentation model output. It must be onehot format and first dim is batch, example shape: [16, 3, 32, 32]. The values should be binarized.y (
Union
[ndarray
,Tensor
]) – ground truth to compute mean the distance. It must be onehot format and first dim is batch. The values should be binarized.include_background (
bool
) – whether to skip distance computation on the first channel of the predicted output. Defaults toFalse
.distance_metric (
str
) – : ["euclidean"
,"chessboard"
,"taxicab"
] the metric used to compute surface distance. Defaults to"euclidean"
.percentile (
Optional
[float
]) – an optional float number between 0 and 100. If specified, the corresponding percentile of the Hausdorff Distance rather than the maximum result will be achieved. Defaults toNone
.directed (
bool
) – whether to calculate directed Hausdorff distance. Defaults toFalse
.

class
monai.metrics.
HausdorffDistanceMetric
(include_background=False, distance_metric='euclidean', percentile=None, directed=False, reduction=<MetricReduction.MEAN: 'mean'>)[source]¶ Compute Hausdorff Distance between two tensors. It can support both multiclasses and multilabels tasks. It supports both directed and nondirected Hausdorff distance calculation. In addition, specify the percentile parameter can get the percentile of the distance. Input y_pred (BNHW[D] where N is number of classes) is compared with ground truth y (BNHW[D]). y_preds is expected to have binarized predictions and y should be in onehot format. You can use suitable transforms in
monai.transforms.post
first to achieve binarized values. Parameters
include_background (
bool
) – whether to include distance computation on the first channel of the predicted output. Defaults toFalse
.distance_metric (
str
) – : ["euclidean"
,"chessboard"
,"taxicab"
] the metric used to compute surface distance. Defaults to"euclidean"
.percentile (
Optional
[float
]) – an optional float number between 0 and 100. If specified, the corresponding percentile of the Hausdorff Distance rather than the maximum result will be achieved. Defaults toNone
.directed (
bool
) – whether to calculate directed Hausdorff distance. Defaults toFalse
.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"
.
Average surface distance¶

monai.metrics.
compute_average_surface_distance
(y_pred, y, include_background=False, symmetric=False, distance_metric='euclidean')[source]¶ This function is used to compute the Average Surface Distance from y_pred to y under the default setting. In addition, if sets
symmetric = True
, the average symmetric surface distance between these two inputs will be returned. Parameters
y_pred (
Union
[ndarray
,Tensor
]) – input data to compute, typical segmentation model output. It must be onehot format and first dim is batch, example shape: [16, 3, 32, 32]. The values should be binarized.y (
Union
[ndarray
,Tensor
]) – ground truth to compute mean the distance. It must be onehot format and first dim is batch. The values should be binarized.include_background (
bool
) – whether to skip distance computation on the first channel of the predicted output. Defaults toFalse
.symmetric (
bool
) – whether to calculate the symmetric average surface distance between seg_pred and seg_gt. Defaults toFalse
.distance_metric (
str
) – : ["euclidean"
,"chessboard"
,"taxicab"
] the metric used to compute surface distance. Defaults to"euclidean"
.

class
monai.metrics.
SurfaceDistanceMetric
(include_background=False, symmetric=False, distance_metric='euclidean', reduction=<MetricReduction.MEAN: 'mean'>)[source]¶ Compute Surface Distance between two tensors. It can support both multiclasses and multilabels tasks. It supports both symmetric and asymmetric surface distance calculation. Input y_pred (BNHW[D] where N is number of classes) is compared with ground truth y (BNHW[D]). y_preds is expected to have binarized predictions and y should be in onehot format. You can use suitable transforms in
monai.transforms.post
first to achieve binarized values. Parameters
include_background (
bool
) – whether to skip distance computation on the first channel of the predicted output. Defaults toFalse
.symmetric (
bool
) – whether to calculate the symmetric average surface distance between seg_pred and seg_gt. Defaults toFalse
.distance_metric (
str
) – : ["euclidean"
,"chessboard"
,"taxicab"
] the metric used to compute surface distance. Defaults to"euclidean"
.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"
.