diff --git a/segmentation_models_pytorch/losses/_functional.py b/segmentation_models_pytorch/losses/_functional.py
index 74301e6d..a26f3f48 100644
--- a/segmentation_models_pytorch/losses/_functional.py
+++ b/segmentation_models_pytorch/losses/_functional.py
@@ -4,6 +4,7 @@
 from typing import Optional
 
 import torch
+import torch.linalg as LA
 import torch.nn.functional as F
 
 __all__ = [
@@ -157,15 +158,7 @@ def soft_jaccard_score(
     dims=None,
 ) -> torch.Tensor:
     assert output.size() == target.size()
-    if dims is not None:
-        intersection = torch.sum(output * target, dim=dims)
-        cardinality = torch.sum(output + target, dim=dims)
-    else:
-        intersection = torch.sum(output * target)
-        cardinality = torch.sum(output + target)
-
-    union = cardinality - intersection
-    jaccard_score = (intersection + smooth) / (union + smooth).clamp_min(eps)
+    jaccard_score = soft_tversky_score(output, target, 1.0, 1.0, smooth, eps, dims)
     return jaccard_score
 
 
@@ -177,13 +170,7 @@ def soft_dice_score(
     dims=None,
 ) -> torch.Tensor:
     assert output.size() == target.size()
-    if dims is not None:
-        intersection = torch.sum(output * target, dim=dims)
-        cardinality = torch.sum(output + target, dim=dims)
-    else:
-        intersection = torch.sum(output * target)
-        cardinality = torch.sum(output + target)
-    dice_score = (2.0 * intersection + smooth) / (cardinality + smooth).clamp_min(eps)
+    dice_score = soft_tversky_score(output, target, 0.5, 0.5, smooth, eps, dims)
     return dice_score
 
 
@@ -196,15 +183,22 @@ def soft_tversky_score(
     eps: float = 1e-7,
     dims=None,
 ) -> torch.Tensor:
+    """Tversky loss
+
+    References:
+        https://arxiv.org/pdf/2302.05666
+        https://arxiv.org/pdf/2303.16296
+
+    """
     assert output.size() == target.size()
-    if dims is not None:
-        intersection = torch.sum(output * target, dim=dims)  # TP
-        fp = torch.sum(output * (1.0 - target), dim=dims)
-        fn = torch.sum((1 - output) * target, dim=dims)
-    else:
-        intersection = torch.sum(output * target)  # TP
-        fp = torch.sum(output * (1.0 - target))
-        fn = torch.sum((1 - output) * target)
+
+    output_sum = torch.sum(output, dim=dims)
+    target_sum = torch.sum(target, dim=dims)
+    difference = LA.vector_norm(output - target, ord=1, dim=dims)
+
+    intersection = (output_sum + target_sum - difference) / 2  # TP
+    fp = output_sum - intersection
+    fn = target_sum - intersection
 
     tversky_score = (intersection + smooth) / (
         intersection + alpha * fp + beta * fn + smooth