diff --git a/mmseg/models/losses/tversky_loss.py b/mmseg/models/losses/tversky_loss.py
index 4ad14f783..96ef92c4d 100644
--- a/mmseg/models/losses/tversky_loss.py
+++ b/mmseg/models/losses/tversky_loss.py
@@ -16,6 +16,7 @@ def tversky_loss(pred,
                  valid_mask,
                  alpha=0.3,
                  beta=0.7,
+                 gamma=1.0,
                  smooth=1,
                  class_weight=None,
                  ignore_index=255):
@@ -31,6 +32,8 @@ def tversky_loss(pred,
                 alpha=alpha,
                 beta=beta,
                 smooth=smooth)
+            if gamma > 1.0:
+                tversky_loss **= (1 / gamma)
             if class_weight is not None:
                 tversky_loss *= class_weight[i]
             total_loss += tversky_loss
@@ -62,7 +65,11 @@ class TverskyLoss(nn.Module):
     """TverskyLoss. This loss is proposed in `Tversky loss function for image
     segmentation using 3D fully convolutional deep networks.
 
-    <https://arxiv.org/abs/1706.05721>`_.
+    <https://arxiv.org/abs/1706.05721>`
+    and `A novel focal Tversky loss function with improved attention U-Net for
+    lesion segmentation.
+
+    <https://arxiv.org/abs/1810.07842>`_.
     Args:
         smooth (float): A float number to smooth loss, and avoid NaN error.
             Default: 1.
@@ -75,6 +82,9 @@ class TverskyLoss(nn.Module):
         beta (float, in [0, 1]):
             The coefficient of false negatives. Default: 0.7.
             Note: alpha + beta = 1.
+        gamma (float, in [1, inf]): The focal term. When `gamma` > 1,
+            the loss focuses more on less accurate predictions that
+            have been misclassified. Default: 1.0.
         loss_name (str, optional): Name of the loss item. If you want this loss
             item to be included into the backward graph, `loss_` must be the
             prefix of the name. Defaults to 'loss_tversky'.
@@ -87,6 +97,7 @@ class TverskyLoss(nn.Module):
                  ignore_index=255,
                  alpha=0.3,
                  beta=0.7,
+                 gamma=1.0,
                  loss_name='loss_tversky'):
         super(TverskyLoss, self).__init__()
         self.smooth = smooth
@@ -94,8 +105,10 @@ class TverskyLoss(nn.Module):
         self.loss_weight = loss_weight
         self.ignore_index = ignore_index
         assert (alpha + beta == 1.0), 'Sum of alpha and beta but be 1.0!'
+        assert gamma >= 1.0, 'gamma should be at least 1.0!'
         self.alpha = alpha
         self.beta = beta
+        self.gamma = gamma
         self._loss_name = loss_name
 
     def forward(self, pred, target, **kwargs):
@@ -117,6 +130,7 @@ class TverskyLoss(nn.Module):
             valid_mask=valid_mask,
             alpha=self.alpha,
             beta=self.beta,
+            gamma=self.gamma,
             smooth=self.smooth,
             class_weight=class_weight,
             ignore_index=self.ignore_index)
diff --git a/tests/test_models/test_losses/test_tversky_loss.py b/tests/test_models/test_losses/test_tversky_loss.py
index 24a4b57e9..70b91e6f1 100644
--- a/tests/test_models/test_losses/test_tversky_loss.py
+++ b/tests/test_models/test_losses/test_tversky_loss.py
@@ -20,6 +20,21 @@ def test_tversky_lose():
         labels = (torch.rand(8, 4, 4) * 3).long()
         tversky_loss(logits, labels, ignore_index=1)
 
+    # test gamma < 1.0
+    with pytest.raises(AssertionError):
+        loss_cfg = dict(
+            type='TverskyLoss',
+            class_weight=[1.0, 2.0, 3.0],
+            loss_weight=1.0,
+            alpha=0.4,
+            beta=0.7,
+            gamma=0.9999,
+            loss_name='loss_tversky')
+        tversky_loss = build_loss(loss_cfg)
+        logits = torch.rand(8, 3, 4, 4)
+        labels = (torch.rand(8, 4, 4) * 3).long()
+        tversky_loss(logits, labels, ignore_index=1)
+
     # test tversky loss
     loss_cfg = dict(
         type='TverskyLoss',