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This function is modified from the official evaluation code of
`CAMELYON 16 Challenge `_, and used to distinguish
true positive and false positive predictions. A true positive prediction is defined when
the detection point is within the annotated ground truth region.
Args:
probs: an array with shape (n,) that represents the probabilities of the detections.
Where, n is the number of predicted detections.
coords: an array with shape (n, n_dim) that represents the coordinates of the detections.
The dimensions must be in the same order as in `evaluation_mask`.
evaluation_mask: the ground truth mask for evaluation.
labels_to_exclude: labels in this list will not be counted for metric calculation.
Returns:
fp_probs: an array that contains the probabilities of the false positive detections.
tp_probs: an array that contains the probabilities of the True positive detections.
num_targets: the total number of targets (excluding `labels_to_exclude`) for all images under evaluation.
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This function is modified from the official evaluation code of
`CAMELYON 16 Challenge `_, and used to distinguish
true positive and false positive predictions. A true positive prediction is defined when
the detection point is within the annotated ground truth region.
Args:
probs: an array with shape (n,) that represents the probabilities of the detections.
Where, n is the number of predicted detections.
y_coord: an array with shape (n,) that represents the Y-coordinates of the detections.
x_coord: an array with shape (n,) that represents the X-coordinates of the detections.
evaluation_mask: the ground truth mask for evaluation.
labels_to_exclude: labels in this list will not be counted for metric calculation.
resolution_level: the level at which the evaluation mask is made.
Returns:
fp_probs: an array that contains the probabilities of the false positive detections.
tp_probs: an array that contains the probabilities of the True positive detections.
num_targets: the total number of targets (excluding `labels_to_exclude`) for all images under evaluation.
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This function is modified from the official evaluation code of
`CAMELYON 16 Challenge `_, and used to compute
the required data for plotting the Free Response Operating Characteristic (FROC) curve.
Args:
fp_probs: an array that contains the probabilities of the false positive detections for all
images under evaluation.
tp_probs: an array that contains the probabilities of the True positive detections for all
images under evaluation.
num_targets: the total number of targets (excluding `labels_to_exclude`) for all images under evaluation.
num_images: the number of images under evaluation.
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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.
Args:
fps_per_image: the average number of false positives per image for different thresholds.
total_sensitivity: sensitivities (true positive rates) for different thresholds.
eval_thresholds: 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.
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