Upload metrics.py

metrics.py

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+"""
+
+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