--- a
+++ b/loss.py
@@ -0,0 +1,40 @@
+import tensorflow.keras.backend as K
+from tensorflow.keras.losses import categorical_crossentropy
+
+
+def generalized_dice(y_true, y_pred):
+    
+    """
+    Generalized Dice Score
+    https://arxiv.org/pdf/1707.03237
+    
+    """
+    
+    y_true    = K.reshape(y_true,shape=(-1,4))
+    y_pred    = K.reshape(y_pred,shape=(-1,4))
+    sum_p     = K.sum(y_pred, -2)
+    sum_r     = K.sum(y_true, -2)
+    sum_pr    = K.sum(y_true * y_pred, -2)
+    weights   = K.pow(K.square(sum_r) + K.epsilon(), -1)
+    generalized_dice = (2 * K.sum(weights * sum_pr)) / (K.sum(weights * (sum_r + sum_p)))
+    
+    return generalized_dice
+
+def generalized_dice_loss(y_true, y_pred):   
+    return 1-generalized_dice(y_true, y_pred)
+    
+    
+def custom_loss(y_true, y_pred):
+    
+    """
+    The final loss function consists of the summation of two losses "GDL" and "CE"
+    with a regularization term.
+    """
+    
+    return generalized_dice_loss(y_true, y_pred) + 1.25 * categorical_crossentropy(y_true, y_pred)
+    
+    
+    
+    
+    
+    
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