--- a
+++ b/metrics/meanspecificity.py
@@ -0,0 +1,44 @@
+import torch
+import numpy as np
+
+
+def get_specificity(SR, GT, threshold=0.5):
+    """
+    cal each img specificity
+    所有负例中被分对的概率
+    结节在在输入图片中所占比例较少 所以该指标的值很高
+    """
+    # Sensitivity == Recall
+    SR = SR > threshold
+    GT = GT == torch.max(GT)
+
+    # TP : True Positive
+    # FN : False Negative
+    # TP = ((SR == 1) + (GT == 1)) == 2
+    # FN = ((SR == 0) + (GT == 1)) == 2
+    # TN : True Negative
+    # FP : False Positive
+    TN = ((SR == 0) + (GT == 0)) == 2
+    FP = ((SR == 1) + (GT == 0)) == 2
+
+    SE = float(torch.sum(TN)) / (float(torch.sum(TN + FP)) + 1e-6)
+
+    return SE
+
+
+def meanspecificity_seg(pred, gt, spes):
+    """
+    :return save img' sensitivity values in sens
+    """
+    gt_tensor = gt
+    gt_tensor = gt_tensor.cpu()
+    pred[pred < 0.5] = 0
+    pred[pred >= 0.5] = 1
+    pred = pred.type(torch.LongTensor)
+    # TO CPU
+    # pred_np = pred.data.cpu().numpy()
+    # gt = gt.data.cpu().numpy()
+    for x in range(pred.size()[0]):
+        spe = get_specificity(pred[x], gt_tensor[x])
+        spes.append(spe)
+    return spes
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