class_docstr: Evaluate with Free Response Operating Characteristic (FROC) score.
  
  Args:
      data: either the list of dictionaries containing probability maps (inference result) and
          tumor mask (ground truth), as below, or the path to a json file containing such list.
          `{
          "prob_map": "path/to/prob_map_1.npy",
          "tumor_mask": "path/to/ground_truth_1.tiff",
          "level": 6,
          "pixel_spacing": 0.243
          }`
      grow_distance: Euclidean distance (in micrometer) by which to grow the label the ground truth's tumors.
          Defaults to 75, which is the equivalent size of 5 tumor cells.
      itc_diameter: the maximum diameter of a region (in micrometer) to be considered as an isolated tumor cell.
          Defaults to 200.
      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.
      nms_sigma: the standard deviation for gaussian filter of non-maximal suppression. Defaults to 0.0.
      nms_prob_threshold: the probability threshold of non-maximal suppression. Defaults to 0.5.
      nms_box_size: the box size (in pixel) to be removed around the pixel for non-maximal suppression.
      image_reader_name: the name of library to be used for loading whole slide imaging, either CuCIM or OpenSlide.
          Defaults to CuCIM.
  
  Note:
      For more info on `nms_*` parameters look at monai.utils.prob_nms.ProbNMS`.
docstr: Evaluate the detection performance of a model based on the model probability map output,
  the ground truth tumor mask, and their associated metadata (e.g., pixel_spacing, level)
docstr_tok: ["Evaluate","the","detection","performance","of","a","model","based","on","the","model","probability","map","output",",","the","ground","truth","tumor","mask",",","and","their","associated","metadata","(","e.g.",",","pixel_spacing",",","level",")"]
code: def evaluate(self):
      """
      Evaluate the detection performance of a model based on the model probability map output,
      the ground truth tumor mask, and their associated metadata (e.g., pixel_spacing, level)
      """
      # compute false positive (FP) and true positive (TP) probabilities for all images
      fp_probs, tp_probs, num_targets, num_images = self.compute_fp_tp()
  
      # compute FROC curve given the evaluation of all images
      fps_per_image, total_sensitivity = compute_froc_curve_data(
          fp_probs=fp_probs,
          tp_probs=tp_probs,
          num_targets=num_targets,
          num_images=num_images,
      )
  
      # compute FROC score give specific evaluation threshold
      froc_score = compute_froc_score(
          fps_per_image=fps_per_image,
          total_sensitivity=total_sensitivity,
          eval_thresholds=self.eval_thresholds,
      )
  
      return froc_score
  
file_path: MONAI/monai/apps/pathology/metrics/lesion_froc.py
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Any, Iterable
  import numpy
  from monai.apps.pathology.utils import PathologyProbNMS, compute_isolated_tumor_cells, compute_multi_instance_mask
  from monai.config import NdarrayOrTensor
  from monai.data.wsi_reader import WSIReader
  from monai.metrics import compute_fp_tp_probs, compute_froc_curve_data, compute_froc_score
  from monai.utils import min_version, optional_import
label_desc: Use image node_id 5 for calling the LesionFROC obj's underlying member method code with example usage: obj.evaluate() and returns: froc_score