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: Prepare the probability map for detection evaluation.
docstr_tok: ["Prepare","the","probability","map","for","detection","evaluation","."]
code: def prepare_inference_result(
      self, sample: dict
  ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
      """
      Prepare the probability map for detection evaluation.
  
      """
      # load the probability map (the result of model inference)
      prob_map = np.load(sample["prob_map"])
  
      # apply non-maximal suppression
      nms_outputs = self.nms(
          probs_map=prob_map, resolution_level=sample["level"]
      )
  
      # separate nms outputs
      probs: Iterable[Any]
      x_coord: Iterable[Any]
      y_coord: Iterable[Any]
      if nms_outputs:
          probs, x_coord, y_coord = zip(*nms_outputs)
      else:
          probs, x_coord, y_coord = [], [], []
  
      return np.array(probs), np.array(x_coord), np.array(y_coord)
  
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 2 for calling the LesionFROC obj's underlying member method code with example usage: obj.prepare_inference_result(sample) and returns: np,  np,  np