Spaces:
Sleeping
Sleeping
File size: 1,344 Bytes
a7c0e2f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
"""
Script includes functions to compute evaluation metrics
"""
import numpy as np
from sklearn.metrics import roc_auc_score
# Function to compute Dice index score for a tissue region indicated by label
def compute_dice(pred, gt, label):
pred = pred.flatten()
gt = gt.flatten()
pred[pred!=label] = 0
pred[pred==label] = 1
gt[gt != label] = 0
gt[gt == label] = 1
pred_pixels = np.sum(pred)
gt_pixels = np.sum(gt)
denom = (pred_pixels + gt_pixels)
if (gt_pixels == 0):
return -1
return np.sum(pred[gt == 1]) * 2.0 / denom
# Function to compute AUC-ROC for a tissue region indicated by label
def compute_auc_roc(pred, gt, label):
pred_binary = np.where(pred == label, 1, 0)
gt_binary = np.where(gt == label, 1, 0)
if (np.sum(gt_binary) == 0 or np.all(gt_binary == 1) or np.all(pred_binary == 1)):
return -1
if (np.sum(pred_binary) == 0):
return 0
pred_flat = pred_binary.flatten()
gt_flat = gt_binary.flatten()
auc_roc = roc_auc_score(gt_flat, pred_flat)
return auc_roc
# Function to compute Accuracy for a tissue region indicated by label
def compute_accuracy_metrics(pred, gt):
correct_predictions = np.sum(pred == gt)
overall_count = pred.size
return correct_predictions, overall_count |